<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Bender, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Encoding and decoding graphical chemical structures as two-dimensional (PDF417) barcodes</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">AMER CHEMICAL SOC</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16TH ST, NW, WASHINGTON, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">572-580</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A wide range of molecular representations exist today, ranging from human-readable structural diagrams over line notations such as Wiswesser Line Notation (WLN) and SMILES to several dozen computer-readable file formats. Still, to encode molecular structures in a computer-readable way for inputting structures in computer systems those formats are not the method of choice since they are not easily and faultlessly readable via optical recognition. In the present study a two-dimensional (PDF417) barcode representation of molecular structures in SMILES format is explored that enables the user to read and input molecular structures into computer systems in a fully automated fashion. A Lempel-Ziv-Welch (LZW) based compressed version of SMILES is suggested for cases where the size of the structure exceeds the storage capacity of PDF417 barcodes. Alternatively, the compact ACS format may be employed as a structural representation. The input via barcodes is fast, practically error free due to the 2D barcodes used which employ error correction and fully automatic. A Web application interface is developed which is able to interpret these barcodes and export them as optimized 3D chemical structures. Applications of this representation range from keeping automated storage systems to Web-based tracking systems of molecular samples. The National Chemical Laboratory, Pune, employs 2D barcode encoded structures for in-house repository management, where barcodes can also be used for querying the database for similar or substructures of the query structure.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;3.657&lt;/p&gt;</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Glen, R. C.</style></author><author><style face="normal" font="default" size="100%">Bender, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">General melting point prediction based on a diverse compound data set and artificial neural networks</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAY</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">3</style></number><publisher><style face="normal" font="default" size="100%">AMER CHEMICAL SOC</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16TH ST, NW, WASHINGTON, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">45</style></volume><pages><style face="normal" font="default" size="100%">581-590</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We report the development of a robust and general model for the prediction of melting points. It is based on a diverse data set of 4173 compounds and employs a large number of 2D and 3D descriptors to capture molecular physicochemical and other graph-based properties. Dimensionality reduction is performed by principal component analysis, while a fully connected feed-forward back-propagation artificial neural network is employed for model generation. The melting point is a fundamental physicochemical property of a molecule that is controlled by both single-molecule properties and intermolecular interactions due to packing in the solid state. Thus, it is difficult to predict, and previously only melting point models for clearly defined and smaller compound sets have been developed. Here we derive the first general model that covers a comparatively large and relevant part of organic chemical space. The final model is based on 2D descriptors, which are found to contain more relevant information than the 3D descriptors calculated. Internal random validation of the model achieves a correlation coefficient of R-2 = 0.661 with an average absolute error of 37.6 degrees C. The model is internally consistent with a correlation coefficient of the test set of Q(2) = 0.658 (average absolute error 38.2 degrees C) and a correlation coefficient of the internal validation set of Q(2) = 0.645 (average absolute error 39.8 degrees C). Additional validation was performed on an external drug data set consisting of 277 compounds. On this external data set a correlation coefficient of Q(2) = 0.662 (average absolute error 32.6 degrees C) was achieved, showing ability of the model to generalize. Compared to an earlier model for the prediction of melting points of druglike compounds our model exhibits slightly improved performance, despite the much larger chemical space covered. The remaining model error is due to molecular properties that are not captured using single-molecule based descriptors, namely both inter- and intramolecular interactions and crystal packing, for which examples of and reasons for outliers are given.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.657</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Krishnan, S.</style></author><author><style face="normal" font="default" size="100%">Pandey, A. K.</style></author><author><style face="normal" font="default" size="100%">Bender, Andreas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Harvesting chemical information from the Internet using a distributed approach: chemxtreme</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">calcination</style></keyword><keyword><style  face="normal" font="default" size="100%">clinker</style></keyword><keyword><style  face="normal" font="default" size="100%">one-dimensional model</style></keyword><keyword><style  face="normal" font="default" size="100%">rotary cement kiln</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAR</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">2</style></number><publisher><style face="normal" font="default" size="100%">ASC, CINF; CSA Trust; CSJ; GDCh; KNCV</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16TH ST, NW, WASHINGTON, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">46</style></volume><pages><style face="normal" font="default" size="100%">452-461</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;{The Internet is a comprehensive resource of chemical information which is at the same time largely unstructured. It provides a wealth of scientific information such as experimental data and requires a suitable automated data mining and analysis tool for its meaningful exploration. The Java based software presented here, ChemXtreme, is developed for harvesting chemical information from the Internet employing the Google API in combination with a distributed client/server text analysis architecture based on JavaRMI. It represents the first and until now the only toolkit for automated structured data retrieval from the Internet which is itself open source. ChemXtreme employs the ``search the search engine''&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><notes><style face="normal" font="default" size="100%">7th International Conference on Chemical Structures, Noordwijkerhout, NETHERLANDS, JUN 05-09, 2005</style></notes><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;3.657&lt;/p&gt;</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kumar, R. Suresh</style></author><author><style face="normal" font="default" size="100%">Prabhune, Asmita</style></author><author><style face="normal" font="default" size="100%">Pundle, A. V.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Suresh, C. G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tryptophan residue is identified in the substrate binding of penicillin G acylase from Kluyvera citrophila</style></title><secondary-title><style face="normal" font="default" size="100%">Enzyme and Microbial Technology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">fluorescence measurement</style></keyword><keyword><style  face="normal" font="default" size="100%">K. citrophila</style></keyword><keyword><style  face="normal" font="default" size="100%">Penicillin G acylase</style></keyword><keyword><style  face="normal" font="default" size="100%">sequence alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">substrate-docking</style></keyword><keyword><style  face="normal" font="default" size="100%">tryptophan modification</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">5</style></number><publisher><style face="normal" font="default" size="100%">ELSEVIER SCIENCE INC</style></publisher><pub-location><style face="normal" font="default" size="100%">360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA</style></pub-location><volume><style face="normal" font="default" size="100%">40</style></volume><pages><style face="normal" font="default" size="100%">1389-1397</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Penicillin acylases are important enzymes in pharmaceutical industry for the production of semi-synthetic beta-lactam antibiotics via the key intermediate 6-aminopenicillanic acid. The penicillin G acylase purified from Kluyvera citrophila (KcPGA) on modification with tryptophan-specific reagents such as N-bromo succinamide (NBS) and 2-hydroxy 5-nitrobenzylbromide (HNBB) showed partial loss of activity and substrate protection. Various solute quenchers and substrate were used to probe the microenvironment of the putative reactive tryptophan through fluorescence quenching. Homology modeling of KcPGA structure has been carried out. Docking substrate on this modeled KcPGA structure identifies the tryptophan residue that is directly influenced by substrate binding. To confirm the biological significance of this particular tryptophan, we did a sequence comparison of PGAs from various organisms. The sequence alignment clustered the matches into two sets, those closer to (&amp;gt; 40% identical) KcPGA and had the tryptophan of interest present in them formed the first set, while those less identical (&amp;lt; 30%) to KcPGA and the particular tryptophan absent in them formed the second set. It is clear from the reported kinetic parameters of representative members of these two sets that the affinity for penicillin G (penG) of the former class is several times better. Thus, based on our studies we suggest that the tryptophan residue in the identified position is important for binding substrate penG by the acylases. (c) 2006 Elsevier Inc. All rights reserved.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">2.624</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Krishnan, S.</style></author><author><style face="normal" font="default" size="100%">Pandey, Anil Kumar</style></author><author><style face="normal" font="default" size="100%">Bender, Andreas</style></author><author><style face="normal" font="default" size="100%">Tropsha, Alexander</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Distributed chemical computing using chemstar: an open source java remote method invocation architecture applied to large scale molecular data from pubchem</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Chemical Information and Modeling</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">4</style></number><publisher><style face="normal" font="default" size="100%">AMER CHEMICAL SOC</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16TH ST, NW, WASHINGTON, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">48</style></volume><pages><style face="normal" font="default" size="100%">691-703</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;present the application of a Java remote method invocation (RMI) based open source architecture to distributed chemical computing. This architecture was previously employed for distributed data harvesting of chemical information from the Internet via the Google application programming interface (API; ChemXtreme). Due to its open source character and its flexibility, the underlying server/client framework can be quickly adopted to virtually every computational task that can be parallelized. Here, we present the server/client communication framework as well as an application to distributed computing of chemical properties on a large scale (currently the size of PubChem; about 18 million compounds), using both the Marvin toolkit as well as the open source JOELib package. As an application, for this set of compounds, the agreement of log P and TPSA between the packages was compared. Outliers were found to be mostly non-druglike compounds and differences could usually be explained by differences in the underlying algorithms. ChemStar is the first open source distributed chemical computing environment built on Java RMI, which is also easily adaptable to user demands due to its ``plug-in architecture''. The complete source codes as well as calculated properties along with links to PubChem resources are available on the Internet via a graphical user interface at http://moltable.nel.res.in/chemstar/.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.657</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Computer vision based chemical information extraction from digital images and streaming videos</style></title><secondary-title><style face="normal" font="default" size="100%">242nd National Meeting of the American-Chemical-Society (ACS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American-Chemical-Society (ACS)</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16th ST, NW, Washington, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">242</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><notes><style face="normal" font="default" size="100%">242nd National Meeting of the American-Chemical-Society (ACS), Denver, CO, AUG 28-SEP 01, 2011</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemInfoCloud: opensource based cloud compatible chemical textmining tools for harvesting largescale medical literature</style></title><secondary-title><style face="normal" font="default" size="100%">248th National Meeting of the American-Chemical-Society (ACS)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American Chemical Society</style></publisher><pub-location><style face="normal" font="default" size="100%">1155 16th ST, NW, Washington, DC 20036 USA</style></pub-location><volume><style face="normal" font="default" size="100%">248</style></volume><pages><style face="normal" font="default" size="100%">Meeting Abstract: 69-CINF</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><work-type><style face="normal" font="default" size="100%">Meeting Abstract</style></work-type><notes><style face="normal" font="default" size="100%">248th National Meeting of the American-Chemical-Society (ACS), San Francisco, CA, AUG 10-14, 2014</style></notes></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chemoinformatics approach for the design and screening of focused virtual libraries</style></title><secondary-title><style face="normal" font="default" size="100%">Practical Chemoinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pub-location><style face="normal" font="default" size="100%">India</style></pub-location><pages><style face="normal" font="default" size="100%">93-131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;It is challenging to handle a large volume of molecular data without appropriate tools. Here, we describe the need and the approaches for the development of focussed virtual libraries to design efficient molecules and optimize them for lead generation. The experimental chemists and biologists are more interested in properties of chemicals and their response to biological system in both beneficial and adverse effects context rather than just their structures. In this chapter, the focus is to relate newly designed chemical structures to their predicted activity, property or toxicity. Property prediction tools save time, money and lives of experimental animals. They come in handy while taking informed decisions especially in certain cases involving pharmacodynamic studies of drug molecules in humans where there are inevitable ethical and safety concerns. Property prediction is an important component in virtual screening which is at the heart of drug design and the most important step where chemoinformatics plays a major role. The other fields where structure–activity relation-based principles hold good for virtual screening are agrochemicals and environmental science, specifically the toxicity and biodegradability prediction of pollutant molecules. In this chapter, we will show how to design software tools to handle generation of focussed virtual libraries from a given set of molecules with common features, fragments or bioactivity spectrum.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">Chemoinformatics Approach for the Design and Screening of focused virtual libraries</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Docking and pharmacophore modelling for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%"> Practical Chemoinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">195 - 269</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Protein and ligand molecules as two separate entities appear and behave differently, but what happens when they come together and interact with each other is one of the interesting facts in modern molecular biology and molecular recognition. This interaction can be well explained with the concept of docking which in a simple way can be described as the study of how a molecule can bind to another molecule to result in a stable entity. The two binding molecules can be either a protein and a ligand or a protein and a protein. Irrespective of which two molecules are interacting, a docking process invariably includes two steps—conformational search through various algorithms and scoring or ranking. Even though prolific research has been carried out in this field, yet it is still a topic of current interest as there is a scope for improvement to rationalize binding interactions with biological function using docking program. This chapter focuses on how to set up and perform docking runs using freeware and commercial software. Most of the known docking protocols like induced fit docking, protein–protein docking, and pharmacophore-based docking have been discussed. The use of pharmacophore queries as filters in virtual screening is also demonstrated using suitable examples.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">Docking and pharmacophore modeling for virtual screening</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Machine learning methods in chemoinformatics for drug discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Practical Chemoinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">133-194</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">It is well known that the structure of a molecule is responsible for its biological activity or physicochemical property. Here, we describe the role of machine learning (ML)/statistical methods for building reliable, predictive models in chemoinformatics. The ML methods are broadly divided into clustering, classification and regression techniques. However, the statistical/mathematical techniques which are part of the ML tools, such as artificial neural networks, hidden Markov models, support vector machine, decision tree learning, Random Forest and Naive Bayes and belief networks, are best suited for drug discovery and play an important role in lead identification and lead optimization steps. This chapter provides stepwise procedures for building ML-based classification and regression models using state-of-art open-source and proprietary tools. A few case studies using benchmark data sets have been carried out to demonstrate the efficacy of the ML-based classification for drug designing</style></abstract><section><style face="normal" font="default" size="100%">Machine Learning Methods in Chemoinformatics for Drug Discovery</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Open-source tools, techniques, and data in chemoinformatics</style></title><secondary-title><style face="normal" font="default" size="100%">Practical Chemoinformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><pages><style face="normal" font="default" size="100%">1-92</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Chemicals are everywhere and they are essentially composed of atoms and bonds that support life and provide comfort. The numerous combinations of these entities lead to the complexity and diversity in the universe. Chemistry is a subject which analyzes and tries to explain this complexity at the atomic level. Advancement in this subject led to more data generation and information explosion. Over a period of time, the observations were recorded in chemical documents that include journals, patents, and research reports. The vast amount of chemical literature covering more than two centuries demands the extensive use of information technology to manage it. Today, the chemoinformatics tools and methods have grown powerful enough to handle and discover unexplored knowledge from this huge resource of chemical information. The role of chemoinformatics is to add value to every bit of chemical data. The underlying theme of this domain is how to develop efficient chemical with predicted physico-chemical and biological properties for economic, social, health, safety, and environment. In this chapter, we begin with a brief definition and role of open-source tools in chemoinformatics and extend the discussion on the need for basic computer knowledge required to understand this specialized and interdisciplinary subject. This is followed by an in-depth analysis of traditional and advanced methods for handling chemical structures in computers which is an elementary but essential precursor for performing any chemoinformatics task. Practical guidance on step-by-step use of open-source, free, academic, and commercial structure representation tools is also provided. To gain a better understanding, it is highly recommended that the reader attempts the practice tutorials, Do it yourself exercises, and questions given in each chapter. The scope of this chapter is designed for experimental chemists, biologists, mathematicians, physicists, computer scientists, etc. to understand the subject in a practical way with relevant and easy-to-understand examples and also to encourage the readers to proceed further with advanced topics in the subsequent chapters.&lt;/p&gt;</style></abstract><section><style face="normal" font="default" size="100%">Open-Source Tools, Techniques, and Data in Chemoinformatics</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Goel, Purva</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Tambe, S. S.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, B. D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method</style></title><secondary-title><style face="normal" font="default" size="100%">Letters in Drug Design &amp; Discovery</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ADME modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">Caco-2 cell permeability</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">MLP</style></keyword><keyword><style  face="normal" font="default" size="100%">SVR</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">9</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">1112-1118</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">0.67</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Nimje, Deepika</style></author><author><style face="normal" font="default" size="100%">Pahujani, Rakhi</style></author><author><style face="normal" font="default" size="100%">Tyagi, Kushal</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Padmakumar, Krishna Pillai</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Chemoinformatics approach for building molecular networks from marine organisms</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemoinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug design</style></keyword><keyword><style  face="normal" font="default" size="100%">marine</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual library</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">673-684</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Natural products obtained from marine sources are considered to be a rich and diverse source of potential drugs. In the present work we demonstrate the use of chemoinformatics approach for the design of new molecules inspired by molecules from marine organisms. Accordingly we have assimilated information from two major scientific domains namely chemoinformatics and biodiversity informatics to develop an interactive marine database named MIMMO (Medicinally Important Molecules from Marine Organisms). The database can be queried for species, molecules, scaffolds, drugs, diseases and associated cumulative biological activity spectrum along with links to the literature resources. Molecular informatics analysis of the molecules obtained from MIMMO was performed to study their chemical space. The distinct skeletal features of the biologically active compounds isolated from marine species were identified. Scaffold molecules and species networks were created to identify common scaffolds from marine source and drug space. An analysis of the entire molecular data revealed a unique list of around 2000 molecules from which ten most frequently occurring distinct scaffolds were obtained.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Pandit, Deepak</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemScreener: a distributed computing tool for scaffold based virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Scaffold extraction</style></keyword><keyword><style  face="normal" font="default" size="100%">therapeutic category</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual library generation</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">544-561</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;In this work we present ChemScreener, a Java-based application to perform virtual library generation combined with virtual screening in a platform-independent distributed computing environment. ChemScreener comprises a scaffold identifier, a distinct scaffold extractor, an interactive virtual library generator as well as a virtual screening module for subsequently selecting putative bioactive molecules. The virtual libraries are annotated with chemophore-, pharmacophore- and toxicophore-based information for compound prioritization. The hits selected can then be further processed using QSAR, docking and other in silico approaches which can all be interfaced within the ChemScreener framework. As a sample application, in this work scaffold selectivity, diversity, connectivity and promiscuity towards six important therapeutic classes have been studied. In order to illustrate the computational power of the application, 55 scaffolds extracted from 161 anti-psychotic compounds were enumerated to produce a virtual library comprising 118 million compounds (17 GB) and annotated with chemophore, pharmacophore and toxicophore based features in a single step which would be non-trivial to perform with many standard software tools today on libraries of this size.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Pandit, Deepak</style></author><author><style face="normal" font="default" size="100%">Bhavasar, Arvind</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemoinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud computing</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular docking</style></keyword><keyword><style  face="normal" font="default" size="100%">OpenVz</style></keyword><keyword><style  face="normal" font="default" size="100%">sequence alignment</style></keyword><keyword><style  face="normal" font="default" size="100%">spectra prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">604-619</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The power of cloud computing and distributed computing has been harnessed to handle vast and heterogeneous data required to be processed in any virtual screening protocol. A cloud computing platorm ChemInfoCloud was built and integrated with several chemoinformatics and bioinformatics tools. The robust engine performs the core chemoinformatics tasks of lead generation, lead optimisation and property prediction in a fast and efficient manner. It has also been provided with some of the bioinformatics functionalities including sequence alignment, active site pose prediction and protein ligand docking. Text mining, NMR chemical shift (1H, 13C) prediction and reaction fingerprint generation modules for efficient lead discovery are also implemented in this platform. We have developed an integrated problem solving cloud environment for virtual screening studies that also provides workflow management, better usability and interaction with end users using container based virtualization, OpenVz.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design and development of new chemoinformatics tools for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">526-527</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Pandit, Yogesh</style></author><author><style face="normal" font="default" size="100%">Pandit, Deepak</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemoinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">cloud computing</style></keyword><keyword><style  face="normal" font="default" size="100%">malaria</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">591-603</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Virtual screening is an indispensable tool to cope with the massive amount of data being tossed by the high throughput omics technologies. With the objective of enhancing the automation capability of virtual screening process a robust portal termed MegaMiner has been built using the cloud computing platform wherein the user submits a text query and directly accesses the proposed lead molecules along with their drug-like, lead-like and docking scores. Textual chemical structural data representation is fraught with ambiguity in the absence of a global identifier. We have used a combination of statistical models, chemical dictionary and regular expression for building a disease specific dictionary. To demonstrate the effectiveness of this approach, a case study on malaria has been carried out in the present work. MegaMiner offered superior results compared to other text mining search engines, as established by F score analysis. A single query term `malaria' in the portlet led to retrieval of related PubMed records, protein classes, drug classes and 8000 scaffolds which were internally processed and filtered to suggest new molecules as potential anti-malarials. The results obtained were validated by docking the virtual molecules into relevant protein targets. It is hoped that MegaMiner will serve as an indispensable tool for not only identifying hidden relationships between various biological and chemical entities but also for building better corpus and ontologies.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Nainaru, Ganesh</style></author><author><style face="normal" font="default" size="100%">Muthukrishnan, Murugan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Binding energy</style></keyword><keyword><style  face="normal" font="default" size="100%">docking</style></keyword><keyword><style  face="normal" font="default" size="100%">Mycobacterium tuberculosis</style></keyword><keyword><style  face="normal" font="default" size="100%">open source drug discovery (OSDD)</style></keyword><keyword><style  face="normal" font="default" size="100%">pharmacophore</style></keyword><keyword><style  face="normal" font="default" size="100%">structure based drug design (SBDD)</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">624-637</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Target based virtual screening has surpassed ligand based virtual screening methods in the recent past mainly as it provides more clues regarding intermolecular interactions and takes into consideration the flexible receptor as well. The current methodology describes a computational strategy of predicting Mycobacterium tuberculosis (M. tuberculosis) binders for five well studied targets representing M. tuberculosis proteome encompassing most of the known mechanisms of action. The diversity of the targets was affirmed by their active site analysis and structural studies. The current approach employed pharmacophore searching, docking and clustering techniques in tandem and was validated by enrichment studies using the available Schrodinger data set consisting of 1000 decoys. The application of this methodology was demonstrated by predicting potential molecular targets for fifty newly synthesized compounds. Cross docking studies on the targets were carried out with 4512 known inhibitors utilizing a high performance computing platform to reveal underlying affinity and promiscuity patterns. Optimum binding energy range for all targets as determined by high throughput docking was found to be -3 to -13 kcal/mol.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Rajamohanan, Pattuparambil Ramanpillai</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Prediction of bioactive compounds using computed NMR chemical shifts</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemical shift</style></keyword><keyword><style  face="normal" font="default" size="100%">fingerprints</style></keyword><keyword><style  face="normal" font="default" size="100%">NMR</style></keyword><keyword><style  face="normal" font="default" size="100%">similarity searching</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">562-576</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;NMR based chemical shifts are an important diagnostic parameter for structure elucidation as they capture rich information related to conformational, electronic and stereochemical arrangement of functional groups in a molecule which is responsible for its activity towards any biological target. The present work discusses the importance of computing NMR chemical shifts from molecular structures. The NMR chemical shift data (experimental or computed) was used to generate fingerprints in binary formats for mapping molecular fragments (as descriptors) and correlating with the bioactivity classes. For this study, chemical shift data derived binary fingerprints were computed for 149 classes and 4800 bioactive molecules. The sensitivity and selectivity of fingerprints in discriminating molecules belonging to different therapeutic categories was assessed using a LibSVM based classifier. An accuracy of 82% for proton and 94% for carbon NMR fingerprints were obtained for anti-psoriatic and anti-psychotic molecules demonstrating the effectiveness of this approach for virtual screening.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Pandit, Deepak</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Protein ligand complex guided approach for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Complexes</style></keyword><keyword><style  face="normal" font="default" size="100%">ligand</style></keyword><keyword><style  face="normal" font="default" size="100%">protein</style></keyword><keyword><style  face="normal" font="default" size="100%">scaffolds</style></keyword><keyword><style  face="normal" font="default" size="100%">sequences</style></keyword><keyword><style  face="normal" font="default" size="100%">similarity score</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">577-590</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The target ligand association data is a rich source of information which is not exploited enough for drug design efforts in virtual screening. A java based open-source toolkit for Protein Ligand Network Extraction (J-ProLiNE) focused on protein-ligand complex analysis with several features integrated in a distributed computing network has been developed. Sequence alignment and similarity search components have been automated to yield local, global alignment scores along with similarity and distance scores. 10000 proteins with co-crystallized ligands from pdb and MOAD databases were extracted and analyzed for revealing relationships between targets, ligands and scaffolds. Through this analysis, we could generate a protein ligand network to identify the promiscuous and selective scaffolds for multiple classes of proteins targets. Using J-ProLiNE we created a 507 x 507 matrix of protein targets and native ligands belonging to six enzyme classes and analyzed the results to elucidate the protein-protein, protein-ligand and ligand-ligand interactions. In yet another application of the J-ProLiNE software, we were able to process kinase related information stored in US patents to construct disease-gene-ligand-scaffold networks. It is hoped that the studies presented here will enable target ligand knowledge based virtual screening for inhibitor design.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Radhamohan, Deepthi</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of chemical reactivity and transition state modeling for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">fingerprints</style></keyword><keyword><style  face="normal" font="default" size="100%">intermediates</style></keyword><keyword><style  face="normal" font="default" size="100%">metabolic pathways</style></keyword><keyword><style  face="normal" font="default" size="100%">product-like score</style></keyword><keyword><style  face="normal" font="default" size="100%">reactant-like score</style></keyword><keyword><style  face="normal" font="default" size="100%">reaction</style></keyword><keyword><style  face="normal" font="default" size="100%">screening</style></keyword><keyword><style  face="normal" font="default" size="100%">synthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">638-657</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product-like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of data and methods in chemoinformatics for virtual screening</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">622-623</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Role of open source tools and resources in virtual screening for drug discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Chemoinformatics</style></keyword><keyword><style  face="normal" font="default" size="100%">kinases</style></keyword><keyword><style  face="normal" font="default" size="100%">open source</style></keyword><keyword><style  face="normal" font="default" size="100%">scaffold</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUL</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">6</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">528-543</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mujahid, Mohammad</style></author><author><style face="normal" font="default" size="100%">Perumal, Yogeeswari</style></author><author><style face="normal" font="default" size="100%">Sriram, D.</style></author><author><style face="normal" font="default" size="100%">Basavanag, U. M. V.</style></author><author><style face="normal" font="default" size="100%">Dıaz-Cervantes, Erik</style></author><author><style face="normal" font="default" size="100%">Cordoba-Bahena, Luis</style></author><author><style face="normal" font="default" size="100%">Robles, Juvencio</style></author><author><style face="normal" font="default" size="100%">Gonnade, Rajesh Ghanshyam</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Krishnan, M. Muthu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Spirochromone-chalcone conjugates as antitubercular agents: synthesis, bio evaluation and molecular modeling studies</style></title><secondary-title><style face="normal" font="default" size="100%">RSC Advances</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A new series of spirochromone annulated chalcone conjugates were synthesized and evaluated for their antitubercular activity against Mycobacterium tuberculosis H37Rv strain. These compounds were subjected to molecular modeling studies using docking and chemoinformatics based approaches. The docking simulations were performed against a range of known receptors for chalcone derived compounds to reveal MTB phosphotyrosine phosphatase B [MtbPtpB] protein as the most probable target based on the high binding affinity scores. Five compounds exhibit significant inhibition, showing minimum inhibitory concentration values i.e. MIC values ranging from 3.13–12.5 μg mL−1. Further analysis of the synthesized compounds with known and in-house developed chemoinformatics tools unequivocally established their potential as anti-tubercular compounds. QSAR modeling revealed a quantitative relationship between biological activities and frontier molecular orbital energies of synthesized compounds. The predictive model can be employed further for virtual screening of new compounds in this series.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">129</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">3.06</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Jain, Esha</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Study of applications of machine learning based classification methods for virtual screening of lead molecules</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Anti-anginal</style></keyword><keyword><style  face="normal" font="default" size="100%">anti-arrythmic</style></keyword><keyword><style  face="normal" font="default" size="100%">anti-bacterial</style></keyword><keyword><style  face="normal" font="default" size="100%">anti-convulsant</style></keyword><keyword><style  face="normal" font="default" size="100%">anti-depressant anti-diabetic</style></keyword><keyword><style  face="normal" font="default" size="100%">binary QSAR</style></keyword><keyword><style  face="normal" font="default" size="100%">chemophore</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">pharmacophore</style></keyword><keyword><style  face="normal" font="default" size="100%">toxicophore</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><number><style face="normal" font="default" size="100%">7</style></number><publisher><style face="normal" font="default" size="100%">BENTHAM SCIENCE PUBL LTD</style></publisher><pub-location><style face="normal" font="default" size="100%">EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES</style></pub-location><volume><style face="normal" font="default" size="100%">18</style></volume><pages><style face="normal" font="default" size="100%">658-672</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The ligand-based virtual screening of combinatorial libraries employs a number of statistical modeling and machine learning methods. A comprehensive analysis of the application of these methods for the diversity oriented virtual screening of biological targets/drug classes is presented here. A number of classification models have been built using three types of inputs namely structure based descriptors, molecular fingerprints and therapeutic category for performing virtual screening. The activity and affinity descriptors of a set of inhibitors of four target classes DHFR, COX, LOX and NMDA have been utilized to train a total of six classifiers viz. Artificial Neural Network (ANN), k nearest neighbor (k-NN), Support Vector Machine (SVM), Naive Bayes (NB), Decision Tree - (DT) and Random Forest - (RF). Among these classifiers, the ANN was found as the best classifier with an AUC of 0.9 irrespective of the target. New molecular fingerprints based on pharmacophore, toxicophore and chemophore (PTC), were used to build the ANN models for each dataset. A good accuracy of 87.27% was obtained using 296 chemophoric binary fingerprints for the COX-LOX inhibitors compared to pharmacophoric (67.82 %) and toxicophoric (70.64 %). The methodology was validated on the classical Ames mutagenecity dataset of 4337 molecules. To evaluate it further, selectivity and promiscuity of molecules from five drug classes viz. anti-anginal, anti-convulsant, anti-depressant, anti-arrhythmic and anti-diabetic were studied. The TPC fingerprints computed for each category were able to capture the drug-class specific features using the k-NN classifier. These models can be useful for selecting optimal molecules for drug design.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3><custom4><style face="normal" font="default" size="100%">1.041</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Jain, Esha</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Computational Biology and Chemistry</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">65</style></volume><pages><style face="normal" font="default" size="100%">37-44</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In order to understand the molecular mechanism underlying any disease, knowledge about the interacting proteins in the disease pathway is essential. The number of revealed protein-protein interactions (PPI) is still very limited compared to the available protein sequences of different organisms. Experiment based high-throughput technologies though provide some data about these interactions, those are often fairly noisy. Computational techniques for predicting protein protein interactions therefore assume significance. 1296 binary fingerprints that encode a combination of structural and geometric properties were developed using the crystallographic data of 15,000 protein complexes in the pdb server. In a case study, these fingerprints were created for proteins implicated in the Type 2 diabetes mellitus disease. The fingerprints were input into a SVM based model for discriminating disease proteins from non disease proteins yielding a classification accuracy of 78.2% (AUC value of 0.78) on an external data set composed of proteins retrieved via text mining of diabetes related literature. A PPI network was constructed and analysed to explore new disease targets. The integrated approach exemplified here has a potential for identifying disease related proteins, functional annotation and other proteomics studies. (C) 2016 Elsevier Ltd. All rights reserved.</style></abstract><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.014</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Cheminformatics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">DEC</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">Article Number: 73</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Digital access to chemical journals resulted in a vast array of molecular information that is now available in the supplementary material files in PDF format. However, extracting this molecular information, generally from a PDF document format is a daunting task. Here we present an approach to harvest 3D molecular data from the supporting information of scientific research articles that are normally available from publisher's resources. In order to demonstrate the feasibility of extracting truly computable molecules from PDF file formats in a fast and efficient manner, we have developed a Java based application, namely ChemEngine. This program recognizes textual patterns from the supplementary data and generates standard molecular structure data (bond matrix, atomic coordinates) that can be subjected to a multitude of computational processes automatically. The methodology has been demonstrated via several case studies on different formats of coordinates data stored in supplementary information files, wherein ChemEngine selectively harvested the atomic coordinates and interpreted them as molecules with high accuracy. The reusability of extracted molecular coordinate data was demonstrated by computing Single Point Energies that were in close agreement with the original computed data provided with the articles. It is envisaged that the methodology will enable large scale conversion of molecular information from supplementary files available in the PDF format into a collection of ready-to-compute molecular data to create an automated workflow for advanced computational processes.</style></abstract><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.949</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Biomedical literature mining for protein-protein interactions analysis using electronic mailing system</style></title><secondary-title><style face="normal" font="default" size="100%">253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR </style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Amer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USA</style></publisher><pub-location><style face="normal" font="default" size="100%"> San Francisco, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Pahujani, Rakhi</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">VIMAL: A chemoinformatics toolkit for design of novel antiviral agents through textmining of scientific literature</style></title><secondary-title><style face="normal" font="default" size="100%">253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR </style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">Amer Chemical Soc, 1155 16TH ST, NW, Washington, DC 20036 USA</style></publisher><pub-location><style face="normal" font="default" size="100%">San Francisco, CA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Goel, Purva</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Tambe, Sanjeev S.</style></author><author><style face="normal" font="default" size="100%">Kulkarni, Bhaskar D.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE-ACM Transactions on Computational Biology and Bioinformatics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Binding energy</style></keyword><keyword><style  face="normal" font="default" size="100%">cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease</style></keyword><keyword><style  face="normal" font="default" size="100%">genetic programming</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">protein-protein interactions</style></keyword><keyword><style  face="normal" font="default" size="100%">symbolic regression</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">27-37</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and low root mean square error (RMSE), and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively, were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a) a predictor of the binding energy of cancer related PPI complexes, and (b) a classifier for discriminating PPI complexes related to cancer from those of other diseases.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">1.955</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CCCTK (Compute Cure for Cancer ToolKit) an open source drug discovery platform for design of novel anti-cancer agents</style></title><secondary-title><style face="normal" font="default" size="100%">255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAR</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACS, 1155 16TH ST, NW, Washington, DC 20036 USA</style></publisher><pub-location><style face="normal" font="default" size="100%">New Orleans, LA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">CCCTK: High performance molecular informatics toolkit for the design of anti-cancer molecule</style></title><secondary-title><style face="normal" font="default" size="100%">256th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nanoscience, Nanotechnology and Beyond</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">AUG</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">American-Chemical-Society (ACS)</style></publisher><pub-location><style face="normal" font="default" size="100%"> Boston, MA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Banik, Gregory</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Nedwed, Karl</style></author><author><style face="normal" font="default" size="100%">Kunitsky, Keith</style></author><author><style face="normal" font="default" size="100%">D'Souza, Michelle</style></author><author><style face="normal" font="default" size="100%">Abshear, Ty</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of stereochemistry on sharing chemical structures</style></title><secondary-title><style face="normal" font="default" size="100%">255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year><pub-dates><date><style  face="normal" font="default" size="100%">MAR</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">ACS, 1155 16TH ST, NW, Washington, DC 20036 USA</style></publisher><pub-location><style face="normal" font="default" size="100%">New Orleans, LA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Exploring energy profiles of protein-protein interactions (PPIs) Using DFT method</style></title><secondary-title><style face="normal" font="default" size="100%">Letters in Drug Design &amp; Discovery</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JUN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">16</style></volume><pages><style face="normal" font="default" size="100%">670-677</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Background: Large-scale &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; landscape characterization &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;protein-protein&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;interactions&lt;/span&gt; (&lt;span class=&quot;hitHilite&quot;&gt;PPIs&lt;/span&gt;) is important &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; understand &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; interaction mechanism and &lt;span class=&quot;hitHilite&quot;&gt;protein-protein&lt;/span&gt; docking methods. &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; experimental methods for detecting &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; landscapes are tedious and &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; existing computational methods require longer simulation time.&lt;br /&gt;
	&lt;br /&gt;
	Objective: &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; objective &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; present work is &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; ascertain &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;profiles&lt;/span&gt; at &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; interface regions in &lt;span class=&quot;hitHilite&quot;&gt;a&lt;/span&gt; rapid manner &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; analyze &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; landscape &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;protein-protein&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;interactions&lt;/span&gt;&lt;br /&gt;
	&lt;br /&gt;
	Methods: &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; atomic coordinates obtained from &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; X-ray and NMR spectroscopy data are considered as inputs &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; compute cumulative &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;profiles&lt;/span&gt; for experimentally validated &lt;span class=&quot;hitHilite&quot;&gt;protein-protein&lt;/span&gt; complexes. &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; energies computed by &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; program were comparable &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; standard molecular dynamics simulations.&lt;br /&gt;
	&lt;br /&gt;
	Results: &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; PPI Profiler not only enables rapid generation &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;energy&lt;/span&gt; &lt;span class=&quot;hitHilite&quot;&gt;profiles&lt;/span&gt; but also facilitates &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; detection &lt;span class=&quot;hitHilite&quot;&gt;of&lt;/span&gt; hot spot residue atoms involved therein.&lt;br /&gt;
	&lt;br /&gt;
	Conclusion: &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; hotspot residues and their computed energies matched with &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; experimentally determined hot spot residues and their energies which correlated well by employing &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; MM/GBSA &lt;span class=&quot;hitHilite&quot;&gt;method&lt;/span&gt;. &lt;span class=&quot;hitHilite&quot;&gt;The&lt;/span&gt; proposed &lt;span class=&quot;hitHilite&quot;&gt;method&lt;/span&gt; can be employed &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; scan entire proteomes across species at an atomic level &lt;span class=&quot;hitHilite&quot;&gt;to&lt;/span&gt; study &lt;span class=&quot;hitHilite&quot;&gt;the&lt;/span&gt; key PPI &lt;span class=&quot;hitHilite&quot;&gt;interactions&lt;/span&gt;.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">6</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;0.953&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Belhekar, Akshay</style></author><author><style face="normal" font="default" size="100%">Gagare, Kumar</style></author><author><style face="normal" font="default" size="100%">Bedse, Ritesh</style></author><author><style face="normal" font="default" size="100%">Bhelkar, Yugandhar</style></author><author><style face="normal" font="default" size="100%">Rajeswari, K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Leukemia cancer detection using image analytics : (comparative study)</style></title><secondary-title><style face="normal" font="default" size="100%">2019 5th International Conference On Computing, Communication, Control And Automation (ICCUBEA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE Xplore</style></publisher><pub-location><style face="normal" font="default" size="100%">Pune, India</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Leukemia is a cancer of white blood cells (WBC). It can be fatal if not detected early. Microscopic images are studied by hematologists for detecting cancer. This manual detection becomes very tedious and time-consuming process. Leukemia if detected in earlier stages, can be cured. But traditional process causes late detection of cancerous cells. Hence in order to minimize the death caused due to late detection, an automated system can be used. This paper proposes an automated system which uses image analytics. Based on image analytics and classification algorithms performed on cell image samples of patients, the proposed system will give correct output. The dataset for experimentation is obtained from TCIA (The Cancer Imaging Archive) repository. The dataset is already pre-processed. An open source tool, &quot;Orange-Data Mining&quot; is used for predictions. In this comparative study, it was found that K-means clustering performs well for segmentation phase and also Neural Networks gives better results for classification phase. We have achieved AUC (area under curve) 0.865, Calculation accuracy (0.838), precision (0.835) and F1(0.836) for neural networks.</style></abstract><custom3><style face="normal" font="default" size="100%">Indian</style></custom3><custom4><style face="normal" font="default" size="100%">NA</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yadav, Amit Kumar</style></author><author><style face="normal" font="default" size="100%">Chilukuri, Harsha</style></author><author><style face="normal" font="default" size="100%">Kumari, Linthoinganbi Raj</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Fernandes, Moneesha</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">p-Nitrophenylcarbonates: a new class of compounds for chemodosimetric colorimetric fluoride anion sensing detectable by the naked eye</style></title><secondary-title><style face="normal" font="default" size="100%">ChemistrySelect</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">colorimetric sensing</style></keyword><keyword><style  face="normal" font="default" size="100%">p-nitrophenyl carbonate</style></keyword><keyword><style  face="normal" font="default" size="100%">selective fluoride detection</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">1830-1833</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;A new class of compounds containing the p-nitrophenylcarbonate motif is reported, that can selectively sense fluoride anions over other halide anions with a detection limit ranging from 0.29 to 0.48 mu M. The fluoride ion acts as nucleophile, leading to the liberation of p-nitrophenol, that is easily detectable and quantifiable colorimetrically.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">5</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;1.716&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Viswanadh, N.</style></author><author><style face="normal" font="default" size="100%">Mujahid, M.</style></author><author><style face="normal" font="default" size="100%">Shirazi, Amir Nasrolahi</style></author><author><style face="normal" font="default" size="100%">Tiwari, Rakesh</style></author><author><style face="normal" font="default" size="100%">Parang, Keykavous</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Muthukrishnan, M.</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-chromone fused α-aminophosphonates as Src kinase inhibitors</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Scientific and Industrial Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">111-117</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">A series of novel chromone/aza-chromone fused α-aminophosphonate derivatives were synthesized in good yields using silica chloride as the catalyst. All the synthesized compounds were tested for their c-Src kinase inhibitory activity. Aza-chromone compound showed Src kinase inhibition with an IC50 value of 15.8 µM. The compounds were subjected to molecular docking and dynamics simulations to study the atomic level interactions with an unphosphorylated proto-oncogenic tyrosine protein kinase Src (PDB code 1Y57) as well as phosphorylated tyrosine protein kinase Src (PDB code 2H8H). Docking and molecular dynamic results revealed phosphorylated Src tyrosine kinase protein better results than unphosphorylated tyrosine Src kinase protein. Chemoinformatics study revealed the compounds had lead like properties. Machine learning (SVR) models were built to study the structure activity correlations. A CC of 0.835 was obtained when the SVR model was applied to the 17 synthesized compounds. It is envisaged that the work will provide guidelines for future drug design efforts for Src kinase inhibitors.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">0.204</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Rajput, Vijay B.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Ramasamy, Sureshkumar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Zebrafish acid ceramidase: expression in Pichia pastoris GS115and biochemical characterization</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Biological Macromolecules</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autoproteolytic processing</style></keyword><keyword><style  face="normal" font="default" size="100%">Pichia pastoris</style></keyword><keyword><style  face="normal" font="default" size="100%">Zebrafish acid ceramidase</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">FEB</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">587-593</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Acid ceramidase (N-acylsphingosine deacylase EC 3.5.1.23; AC) catalyzes the hydrolysis of ceramide into sphingosine (SPH) and free fatty acid. Zebrafish acid ceramidase (AC) has 60% homology with the human AC). Mutations in the human AC gene asah1 are known to cause Farber disease and spinal muscular atrophy with progressive myoclonic epilepsy. Zebrafish AC was overexpressed in Pichia pastoris by inserting asah1b gene into the genome. The majority of the overexpressed enzyme was secreted into the culture medium and purified to apparent homogeneity by stepwise chromatography. The recombinant protein was glycosylated precursor, that further undergoes limited autoproteolytic processing into two subunits (alpha and beta) which are visible in SDS-PAGE. The zebrafish AC is heterodimer associated with an inter-subunit disulfide bond. SDS-PAGE estimated the mass of native enzyme to be approximately 50 kDa &amp;amp; size exclusion chromatography estimated the mass of the active enzyme as approximately 100 kDa, suggesting the formation of a dimer of heterodimers. The protein was secreted as a mixture of processed and unprocessed forms in the culture media. A preliminary characterization of purified zebrafish AC was done by an enzyme assay. The zebrafish AC expressed in Pichia pastoris would be used for further structural and functional analysis. (C) 2018 Elsevier B.V. All rights reserved.&lt;/p&gt;</style></abstract><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">Foreign</style></custom3><custom4><style face="normal" font="default" size="100%">3.909</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Karade, Divya</style></author><author><style face="normal" font="default" size="100%">Vijayasarathi, Durairaj</style></author><author><style face="normal" font="default" size="100%">Kadoo, Narendra</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author><author><style face="normal" font="default" size="100%">Ingle, P. K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Design of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants</style></title><secondary-title><style face="normal" font="default" size="100%">Combinatorial Chemistry &amp; High Throughput Screening</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Drugs</style></keyword><keyword><style  face="normal" font="default" size="100%">medicinal plants</style></keyword><keyword><style  face="normal" font="default" size="100%">Metabolites</style></keyword><keyword><style  face="normal" font="default" size="100%">scaffolds</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual libraries</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">JAN</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1113-1131</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Background: Several medicinal plants are being used in Indian medicine systems from ancient times. However, in most cases, the specific molecules or the active ingredients responsible for the medicinal or therapeutic properties are not yet known. Objective: This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach. Methods: We built a database of the Indian medicinal and aromatic plants coupled with associated information (plant families, plant parts used for the medicinal purpose, structural information, therapeutic properties, etc.) We also developed a Java-based chemoinformatics open-source tool called DoMINE (Database of Medicinally Important Natural products from plantaE) for the generation of virtual library and screening of novel molecules from known medicinal plant molecules. We employed chemoinformatics approaches to in-silico screened metabolites from 104 Indian medicinal and aromatic plants and designed novel drug-like bioactive molecules. For this purpose, 1665 ring containing molecules were identified by text mining of literature related to the medicinal plant species, which were later used to extract 209 molecular scaffolds. Different scaffolds were further used to build a focused virtual library. Virtual screening was performed with cluster analysis to predict drug-like and lead-like molecules from these plant molecules in the context of drug discovery. The predicted drug-like and lead-like molecules were evaluated using chemoinformatics approaches and statistical parameters, and only the most significant molecules were proposed as the candidate molecules to develop new drugs. Results and Conclusion: The supra network of molecules and scaffolds identifies the relationship between the plant molecules and drugs. Cluster analysis of virtual library molecules showed that novel molecules had more pharmacophoric properties than toxicophoric and chemophoric properties. We also developed the DoMINE toolkit for the advancement of natural product-based drug discovery through chemoinformatics approaches. This study will be useful in developing new drug molecules from the known medicinal plant molecules. Hence, this work will encourage experimental organic chemists to synthesize these molecules based on the predicted values. These synthesized molecules need to be subjected to biological screening to identify potential molecules for drug discovery research.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">10</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;1.195&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Qazi, Sahar</style></author><author><style face="normal" font="default" size="100%">Jit, Bimal Prasad</style></author><author><style face="normal" font="default" size="100%">Das, Abhishek</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Saxena, Amit</style></author><author><style face="normal" font="default" size="100%">Ray, M. D.</style></author><author><style face="normal" font="default" size="100%">Singh, Angel Rajan</style></author><author><style face="normal" font="default" size="100%">Raza, Khalid</style></author><author><style face="normal" font="default" size="100%">Jayaram, B.</style></author><author><style face="normal" font="default" size="100%">Sharma, Ashok</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BESFA: bioinformatics based evolutionary, structural &amp; functional analysis of prostate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs</style></title><secondary-title><style face="normal" font="default" size="100%">Heliyon</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">(MMGBSA)</style></keyword><keyword><style  face="normal" font="default" size="100%">Adaptive divergence</style></keyword><keyword><style  face="normal" font="default" size="100%">Evolution</style></keyword><keyword><style  face="normal" font="default" size="100%">generalized born surface area</style></keyword><keyword><style  face="normal" font="default" size="100%">Homology</style></keyword><keyword><style  face="normal" font="default" size="100%">Mechanics</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular docking</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular dynamic simulation molecular</style></keyword><keyword><style  face="normal" font="default" size="100%">POTE paralogs</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">e10476</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	The POTE family comprises 14 paralogues and is primarily expressed in Prostate, Placenta, Ovary, Testis, Embryo (POTE), and cancerous cells. The prospective function of the POTE protein family under physiological conditions is less understood. We systematically analyzed their cellular localization and molecular docking analysis to elucidate POTE proteins' structure, function, and Adaptive Divergence. Our results suggest that group three POTE paralogs (POTEE, POTEF, POTEI, POTEJ, and POTEKP (a pseudogene)) exhibits significant variation among other members could be because of their Adaptive Divergence. Furthermore, our molecular docking studies on POTE protein revealed the highest binding affinity with NCI-approved anticancer compounds. Additionally, POTEE, POTEF, POTEI, and POTEJ were subject to an explicit molecular dynamic simulation for 50ns. MM-GBSA and other essential electrostatics were calculated that showcased that only POTEE and POTEF have absolute binding affinities with minimum energy exploitation. Thus, this study's outcomes are expected to drive cancer research to successful utilization of POTE genes family as a new biomarker, which could pave the way for the discovery of new therapies.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">9</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	3.776&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shaikh, Nilofer</style></author><author><style face="normal" font="default" size="100%">Bapat, Sanket</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Review on computational analysis of big data in breast cancer for predicting potential biomarkers</style></title><secondary-title><style face="normal" font="default" size="100%">Current Topics in Medicinal Chemistry</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Big data</style></keyword><keyword><style  face="normal" font="default" size="100%">Biomarkers</style></keyword><keyword><style  face="normal" font="default" size="100%">Breast cancer</style></keyword><keyword><style  face="normal" font="default" size="100%">Driver genes</style></keyword><keyword><style  face="normal" font="default" size="100%">Network analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">text mining</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2022</style></year><pub-dates><date><style  face="normal" font="default" size="100%">SEP</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">1793-1810</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">21</style></issue><work-type><style face="normal" font="default" size="100%">Review</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	3.570&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Shaikh, Nilofer</style></author><author><style face="normal" font="default" size="100%">Linthoi, R. K.</style></author><author><style face="normal" font="default" size="100%">Swamy, V, K.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Vyas, Renu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Biomolecular Structure and Dynamics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">chemotherapeutic drugs</style></keyword><keyword><style  face="normal" font="default" size="100%">Drug repurposing</style></keyword><keyword><style  face="normal" font="default" size="100%">Molecular docking</style></keyword><keyword><style  face="normal" font="default" size="100%">molecular simulation (MD)</style></keyword><keyword><style  face="normal" font="default" size="100%">structure-based drug designing (SBDD)</style></keyword><keyword><style  face="normal" font="default" size="100%">virtual screening</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">NOV </style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">41</style></volume><pages><style face="normal" font="default" size="100%">7735-7743</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Drug repurposing is a method to identify novel therapeutic agents from the existing drugs and clinical compounds. In the present comprehensive work, molecular docking, virtual screening and dynamics simulations were carried out for ten cancer types viz breast, colon, central nervous system, leukaemia, melanoma, ovarian, prostate, renal and lung (non-small and small cell) against validated eighteen kinase targets. The study aims to understand the action of chemotherapy drugs mechanism through binding interactions against selected targets via comparative docking simulations with the state-art molecular modelling suits such as MOE, Cresset-Flare, AutoDock Vina, GOLD and GLIDE. Chemotherapeutic drugs (n = 112) were shortlisted from standard drug databases with appropriate chemoinformatic filters. Based on docking studies it was revealed that leucovorin, nilotinib, ellence, thalomid and carfilzomib drugs possessed potential against other cancer targets. A library was built to enumerate novel molecules based on the scaffold and functional groups extracted from known drugs and clinical compounds. Twenty novel molecules were prioritised further based on drug-like attributes. These were cross docked against 1MQ4 Aurora-A Protein Kinase for prostate cancer and 4UYA Mitogen-activated protein kinase for renal cancer. All docking programs yielded similar results but interestingly AutoDock Vina yielded the lowest RMSD with the native ligand. To further validate the final docking results at atomistic level, molecular dynamics simulations were performed to ascertain the stability of the protein-ligand complex. The study enables repurposing of drugs and lead identification by employing a host of structure and ligand based virtual screening tools and techniques. Communicated by Ramaswamy H. Sarma&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">16</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	4.4&lt;/p&gt;
</style></custom4></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dayama, Bhakti R.</style></author><author><style face="normal" font="default" size="100%">Mahadik, Varsha A.</style></author><author><style face="normal" font="default" size="100%">Somani, Deepika</style></author><author><style face="normal" font="default" size="100%">Shinde, Balkrishna A.</style></author><author><style face="normal" font="default" size="100%">Kondhare, Kirtikumar R.</style></author><author><style face="normal" font="default" size="100%">Karthikeyan, Muthukumarasamy</style></author><author><style face="normal" font="default" size="100%">Kadoo, Narendra Y.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transcriptome analyses reveal TaWRKY41 as a potential candidate governing spot blotch resistance in wheat</style></title><secondary-title><style face="normal" font="default" size="100%">Physiology and Molecular Biology of Plants</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Plant defense</style></keyword><keyword><style  face="normal" font="default" size="100%">plant-pathogen interaction</style></keyword><keyword><style  face="normal" font="default" size="100%">TaWRKY41</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcription factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Transcriptomics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">APR</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">591-608</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Spot blotch disease caused by Bipolaris sorokiniana poses a significant threat to wheat production. Cultivation of disease-resistant wheat genotypes appears to be the most practical approach to mitigate the impact of this devastating disease. However, the molecular responses of wheat plants during spot blotch disease progression remain poorly understood. This study employed RNA-sequencing to unravel the spatiotemporal molecular events underlying the resistance mechanism in the spot blotch susceptible and resistant wheat genotypes. This study further provides a comprehensive overview of differentially expressed transcripts through functional analysis and transcription factor identification, elucidating the biological mechanisms governing wheat-B. sorokiniana interaction. In the resistant genotype, the expression of one of the key transcription factors, TaWRKY41, was significantly induced upon pathogen inoculation. Computational studies, electrophoretic-mobility shift assay, and yeast one-hybrid assay confirmed the interaction of the recombinant TaWRKY41 protein with W-box elements present in the promoters of plant defense-related genes. Furthermore, co-expression network analyses identified downstream genes positively correlated with TaWRKY41, providing insights into their probable involvement in the defense response. Overall, our investigation suggests that TaWRKY41 contributes to spot blotch resistance in wheat. This knowledge can help develop new disease-resistant wheat varieties.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue><work-type><style face="normal" font="default" size="100%">Article</style></work-type><custom3><style face="normal" font="default" size="100%">&lt;p&gt;
	Foreign&lt;/p&gt;
</style></custom3><custom4><style face="normal" font="default" size="100%">&lt;p&gt;
	3.9&lt;/p&gt;
</style></custom4></record></records></xml>