02219nas a2200157 4500008004100000022001400041245009300055210006900148260007100217300001200288490000700300520157600307100003301883700002001916856012501936 2005 eng d a1549-959600aEncoding and decoding graphical chemical structures as two-dimensional (PDF417) barcodes0 aEncoding and decoding graphical chemical structures as twodimens a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcMAY a572-5800 v453 a

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.

1 aKarthikeyan, Muthukumarasamy1 aBender, Andreas uhttps://library.ncl.res.in/content/encoding-and-decoding-graphical-chemical-structures-two-dimensional-pdf417-barcodes-002832nas a2200169 4500008004100000022001400041245010500055210006900160260007100229300001200300490000700312520214500319100003302464700001702497700002002514856012802534 2005 eng d a1549-959600aGeneral melting point prediction based on a diverse compound data set and artificial neural networks0 aGeneral melting point prediction based on a diverse compound dat a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcMAY a581-5900 v453 a

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.

1 aKarthikeyan, Muthukumarasamy1 aGlen, R., C.1 aBender, Andreas uhttps://library.ncl.res.in/content/general-melting-point-prediction-based-diverse-compound-data-set-and-artificial-neural-001570nas a2200229 4500008004100000022001400041245009500055210006900150260009100219300001200310490000700322520072500329653001601054653001201070653002601082653002301108100003301131700001701164700001901181700002001200856012001220 2006 eng d a0887-062400aHarvesting chemical information from the Internet using a distributed approach: chemxtreme0 aHarvesting chemical information from the Internet using a distri a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbASC, CINF; CSA Trust; CSJ; GDCh; KNCVcMAR a452-4610 v463 a

{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''

10acalcination10aclinker10aone-dimensional model10arotary cement kiln1 aKarthikeyan, Muthukumarasamy1 aKrishnan, S.1 aPandey, A., K.1 aBender, Andreas uhttps://library.ncl.res.in/content/harvesting-chemical-information-internet-using-distributed-approach-chemxtreme-102563nas a2200265 4500008004100000022001400041245011100055210006900166260007900235300001400314490000700328520157900335653002901914653001801943653002501961653002301986653002202009653002802031100001802059700002102077700001902098700003302117700001902150856012802169 2007 eng d a0141-022900aTryptophan residue is identified in the substrate binding of penicillin G acylase from Kluyvera citrophila0 aTryptophan residue is identified in the substrate binding of pen a360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USAbELSEVIER SCIENCE INCcAPR a1389-13970 v403 a

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 (> 40% identical) KcPGA and had the tryptophan of interest present in them formed the first set, while those less identical (< 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.

10afluorescence measurement10aK. citrophila10aPenicillin G acylase10asequence alignment10asubstrate-docking10atryptophan modification1 aKumar, Suresh1 aPrabhune, Asmita1 aPundle, A., V.1 aKarthikeyan, Muthukumarasamy1 aSuresh, C., G. uhttps://library.ncl.res.in/content/tryptophan-residue-identified-substrate-binding-penicillin-g-acylase-kluyvera-citrophila02240nas a2200193 4500008004100000022001400041245016000055210006900215260007100284300001200355490000700367520142600374100003301800700001701833700002401850700002001874700002301894856012901917 2008 eng d a1549-959600aDistributed chemical computing using chemstar: an open source java remote method invocation architecture applied to large scale molecular data from pubchem0 aDistributed chemical computing using chemstar an open source jav a1155 16TH ST, NW, WASHINGTON, DC 20036 USAbAMER CHEMICAL SOCcAPR a691-7030 v483 a

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/.

1 aKarthikeyan, Muthukumarasamy1 aKrishnan, S.1 aPandey, Anil, Kumar1 aBender, Andreas1 aTropsha, Alexander uhttps://library.ncl.res.in/content/distributed-chemical-computing-using-chemstar-open-source-java-remote-method-invocation-000602nas a2200121 4500008004100000022001400041245009900055210006900154260008500223490000800308100003300316856013100349 2011 eng d a0065-772700aComputer vision based chemical information extraction from digital images and streaming videos0 aComputer vision based chemical information extraction from digit a1155 16th ST, NW, Washington, DC 20036 USAbAmerican-Chemical-Society (ACS)cAUG0 v2421 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/computer-vision-based-chemical-information-extraction-digital-images-and-streaming-videos-000658nas a2200133 4500008004100000022001400041245012400055210006900179260007900248300003000327490000800357100003300365856012600398 2014 eng d a0065-772700aChemInfoCloud: opensource based cloud compatible chemical textmining tools for harvesting largescale medical literature0 aChemInfoCloud opensource based cloud compatible chemical textmin a1155 16th ST, NW, Washington, DC 20036 USAbAmerican Chemical SocietycAUG aMeeting Abstract: 69-CINF0 v2481 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/cheminfocloud-opensource-based-cloud-compatible-chemical-textmining-tools-harvesting-001999nas a2200133 4500008004100000245008800041210006900129260002000198300001100218520147600229100003301705700001501738856011201753 2014 eng d00aChemoinformatics approach for the design and screening of focused virtual libraries0 aChemoinformatics approach for the design and screening of focuse aIndiabSpringer a93-1313 a

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.

1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/chemoinformatics-approach-design-and-screening-focused-virtual-libraries01742nas a2200121 4500008004100000245006200041210006200103260001300165300001400178520130200192100003301494856009301527 2014 eng d00aDocking and pharmacophore modelling for virtual screening0 aDocking and pharmacophore modelling for virtual screening bSpringer a195 - 2693 a

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.

1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/docking-and-pharmacophore-modelling-virtual-screening01486nas a2200133 4500008004100000245006800041210006800109260001300177300001200190520100600202100003301208700001501241856009601256 2014 eng d00aMachine learning methods in chemoinformatics for drug discovery0 aMachine learning methods in chemoinformatics for drug discovery bSpringer a133-1943 aIt 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 designing1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/machine-learning-methods-chemoinformatics-drug-discovery02616nas a2200121 4500008004100000245006400041210006100105260001300166300000900179520217900188100003302367856009402400 2014 eng d00aOpen-source tools, techniques, and data in chemoinformatics0 aOpensource tools techniques and data in chemoinformatics bSpringer a1-923 a

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.

1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/open-source-tools-techniques-and-data-chemoinformatics02302nas a2200253 4500008004100000022001400041245009500055210006900150260011100219300001400330490000700344520138000351653001801731653002901749653002401778653000801802653000801810100001501818700001601833700003301849700001801882700002101900856012701921 2014 eng d a1570-180800aPharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method0 aPharmacokinetic modeling of caco2 cell permeability using geneti aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcNOV a1112-11180 v113 a

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.

10aADME modeling10aCaco-2 cell permeability10agenetic programming10aMLP10aSVR1 aVyas, Renu1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, S., S.1 aKulkarni, B., D. uhttps://library.ncl.res.in/content/pharmacokinetic-modeling-caco-2-cell-permeability-using-genetic-programming-gp-method-002142nas a2200265 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520120300338653002101541653001601562653001101578653002001589100003301609700001901642700002001661700001801681700001801699700001501717700003201732856011201764 2015 eng d a1386-207300aChemoinformatics approach for building molecular networks from marine organisms0 aChemoinformatics approach for building molecular networks from m aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a673-6840 v183 a

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.

10aChemoinformatics10aDrug design10amarine10avirtual library1 aKarthikeyan, Muthukumarasamy1 aNimje, Deepika1 aPahujani, Rakhi1 aTyagi, Kushal1 aBapat, Sanket1 aVyas, Renu1 aPadmakumar, Krishna, Pillai uhttps://library.ncl.res.in/content/chemoinformatics-approach-building-molecular-networks-marine-organisms-002120nas a2200217 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520128100338653002401619653002501643653003101668653002201699100003301721700001901754700001501773856011401788 2015 eng d a1386-207300aChemScreener: a distributed computing tool for scaffold based virtual screening0 aChemScreener a distributed computing tool for scaffold based vir aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a544-5610 v183 a

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.

10aScaffold extraction10atherapeutic category10avirtual library generation10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aVyas, Renu uhttps://library.ncl.res.in/content/chemscreener-distributed-computing-tool-scaffold-based-virtual-screening-002057nas a2200289 4500008004100000022001400041245010400055210006900159260011100228300001200339490000700351520101200358653002101370653002001391653002101411653002201432653001101454653002301465653002301488653001601511653002201527100003301549700001901582700002101601700001501622856013001637 2015 eng d a1386-207300aDesign and development of cheminfocloud: an integrated cloud enabled platform for virtual screening0 aDesign and development of cheminfocloud an integrated cloud enab aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a604-6190 v183 a

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.

10aChemoinformatics10acloud computing10amachine learning10aMolecular docking10aOpenVz10asequence alignment10aspectra prediction10atext mining10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aBhavasar, Arvind1 aVyas, Renu uhttps://library.ncl.res.in/content/design-and-development-cheminfocloud-integrated-cloud-enabled-platform-virtual-screening-000636nas a2200145 4500008004100000022001400041245007900055210006900134260011100203300001200314490000700326100003300333700001500366856010900381 2015 eng d a1386-207300aDesign and development of new chemoinformatics tools for virtual screening0 aDesign and development of new chemoinformatics tools for virtual aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a526-5270 v181 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/design-and-development-new-chemoinformatics-tools-virtual-screening-002426nas a2200241 4500008004100000022001400041245012700055210006900182260011100251300001200362490000700374520149700381653002101878653002001899653001201919653001601931653002201947100003301969700001902002700001902021700001502040856012902055 2015 eng d a1386-207300aMegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment0 aMegaMiner a tool for lead identification through text mining usi aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a591-6030 v183 a

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.

10aChemoinformatics10acloud computing10amalaria10atext mining10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Yogesh1 aPandit, Deepak1 aVyas, Renu uhttps://library.ncl.res.in/content/megaminer-tool-lead-identification-through-text-mining-using-chemoinformatics-tools-and-002226nas a2200253 4500008004100000022001400041245010200055210006900157260011100226300001200337490000700349520123600356653001901592653001201611653003101623653003801654653001801692653003901710100001501749700003301764700002001797700002701817856012801844 2015 eng d a1386-207300aPharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets0 aPharmacophore and docking based virtual screening of validated m aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG a624-6370 v183 a

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.

10aBinding energy10adocking10aMycobacterium tuberculosis10aopen source drug discovery (OSDD)10apharmacophore10astructure based drug design (SBDD)1 aVyas, Renu1 aKarthikeyan, Muthukumarasamy1 aNainaru, Ganesh1 aMuthukrishnan, Murugan uhttps://library.ncl.res.in/content/pharmacophore-and-docking-based-virtual-screening-validated-mycobacterium-tuberculosis-001949nas a2200229 4500008004100000022001400041245007300055210006900128260011100197300001200308490000700320520110300327653001901430653001701449653000801466653002501474653002201499100003301521700004301554700001501597856010701612 2015 eng d a1386-207300aPrediction of bioactive compounds using computed NMR chemical shifts0 aPrediction of bioactive compounds using computed NMR chemical sh aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a562-5760 v183 a

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.

10aChemical shift10afingerprints10aNMR10asimilarity searching10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aRajamohanan, Pattuparambil, Ramanpilla1 aVyas, Renu uhttps://library.ncl.res.in/content/prediction-bioactive-compounds-using-computed-nmr-chemical-shifts-002219nas a2200253 4500008004100000022001400041245006500055210006500120260011100185300001200296490000700308520137700315653001401692653001101706653001201717653001401729653001401743653002101757653002201778100003301800700001901833700001501852856009801867 2015 eng d a1386-207300aProtein ligand complex guided approach for virtual screening0 aProtein ligand complex guided approach for virtual screening aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a577-5900 v183 a

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.

10aComplexes10aligand10aprotein10ascaffolds10asequences10asimilarity score10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Deepak1 aVyas, Renu uhttps://library.ncl.res.in/content/protein-ligand-complex-guided-approach-virtual-screening-002540nas a2200301 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520149700338653001701835653001801852653002301870653002301893653002401916653001301940653001401953653001401967653002201981100003302003700001502036700002302051700002402074700002602098856011402124 2015 eng d a1386-207300aRole of chemical reactivity and transition state modeling for virtual screening0 aRole of chemical reactivity and transition state modeling for vi aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a638-6570 v183 a

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.

10afingerprints10aintermediates10ametabolic pathways10aproduct-like score10areactant-like score10areaction10ascreening10asynthesis10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu1 aTambe, Sanjeev, S.1 aRadhamohan, Deepthi1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/role-chemical-reactivity-and-transition-state-modeling-virtual-screening-000617nas a2200145 4500008004100000022001400041245007100055210006900126260011100195300001200306490000700318100003300325700001500358856009800373 2015 eng d a1386-207300aRole of data and methods in chemoinformatics for virtual screening0 aRole of data and methods in chemoinformatics for virtual screeni aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a622-6230 v181 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/role-data-and-methods-chemoinformatics-virtual-screening-002126nas a2200217 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520132700338653002101665653001201686653001601698653001301714653002201727100003301749700001501782856011101797 2015 eng d a1386-207300aRole of open source tools and resources in virtual screening for drug discovery0 aRole of open source tools and resources in virtual screening for aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJUL a528-5430 v183 a

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.

10aChemoinformatics10akinases10aopen source10ascaffold10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/role-open-source-tools-and-resources-virtual-screening-drug-discovery-001975nas a2200241 4500008004100000245012100041210006900162260000800231490000600239520110700245100002201352700002401374700001501398700002501413700002601438700002501464700002101489700003101510700003301541700001501574700002001589856012401609 2015 eng d00aSpirochromone-chalcone conjugates as antitubercular agents: synthesis, bio evaluation and molecular modeling studies0 aSpirochromonechalcone conjugates as antitubercular agents synthe cDEC0 v53 a

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.

1 aMujahid, Mohammad1 aPerumal, Yogeeswari1 aSriram, D.1 aBasavanag, U., M. V.1 aDıaz-Cervantes, Erik1 aCordoba-Bahena, Luis1 aRobles, Juvencio1 aGonnade, Rajesh, Ghanshyam1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu1 aKrishnan, Muthu uhttps://library.ncl.res.in/content/spirochromone-chalcone-conjugates-antitubercular-agents-synthesis-bio-evaluation-and02881nas a2200325 4500008004100000022001400041245011500055210006900170260011100239300001200350490000700362520173200369653001702101653001902118653001902137653002002156653003402176653001602210653001502226653002102241653001802262653001602280100001502296700001802311700001502329700002302344700003302367700002602400856012902426 2015 eng d a1386-207300aStudy of applications of machine learning based classification methods for virtual screening of lead molecules0 aStudy of applications of machine learning based classification m aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcAUG a658-6720 v183 a

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.

10aAnti-anginal10aanti-arrythmic10aanti-bacterial10aanti-convulsant10aanti-depressant anti-diabetic10abinary QSAR10achemophore10amachine learning10apharmacophore10atoxicophore1 aVyas, Renu1 aBapat, Sanket1 aJain, Esha1 aTambe, Sanjeev, S.1 aKarthikeyan, Muthukumarasamy1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/study-applications-machine-learning-based-classification-methods-virtual-screening-lead-002122nas a2200193 4500008004100000245016100041210006900202260000800271300001000279490000700289520137600296100001501672700001801687700001501705700003301720700001901753700002601772856013001798 2016 eng d00aBuilding and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis0 aBuilding and analysis of proteinprotein interactions related to  cDEC a37-440 v653 aIn 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.1 aVyas, Renu1 aBapat, Sanket1 aJain, Esha1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/building-and-analysis-protein-protein-interactions-related-diabetes-mellitus-using-support02148nas a2200145 4500008004100000245008700041210006900128260000800197300002300205490000600228520160700234100003301841700001501874856011301889 2016 eng d00aChemEngine: harvesting 3D chemical structures of supplementary data from PDF files0 aChemEngine harvesting 3D chemical structures of supplementary da cDEC aArticle Number: 730 v83 aDigital 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.1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/chemengine-harvesting-3d-chemical-structures-supplementary-data-pdf-files00598nas a2200109 4500008004100000245010700041210006900148260009200217100003300309700001500342856013100357 2017 eng d00aBiomedical literature mining for protein-protein interactions analysis using electronic mailing system0 aBiomedical literature mining for proteinprotein interactions ana a San Francisco, CAbAmer chemical soc, 1155 16TH ST, NW, Washington, DC 20036 USAcAPR 1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/biomedical-literature-mining-protein-protein-interactions-analysis-using-electronic-mailing00640nas a2200121 4500008004100000245011900041210006900160260009100229100003300320700002000353700001500373856013000388 2017 eng d00aVIMAL: A chemoinformatics toolkit for design of novel antiviral agents through textmining of scientific literature0 aVIMAL A chemoinformatics toolkit for design of novel antiviral a aSan Francisco, CAbAmer Chemical Soc, 1155 16TH ST, NW, Washington, DC 20036 USAcAPR 1 aKarthikeyan, Muthukumarasamy1 aPahujani, Rakhi1 aVyas, Renu uhttps://library.ncl.res.in/content/vimal-chemoinformatics-toolkit-design-novel-antiviral-agents-through-textmining-scientific02246nas a2200289 4500008004100000022001400041245013800055210006900193260000800262300001000270490000700280520126700287653001901554653001101573653001201584653002401596653002101620653003301641653002401674100001501698700001801713700001601731700003301747700002301780700002601803856012701829 2018 eng d a1545-596300aApplication of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data0 aApplication of genetic programming GP formalism for building dis cFEB a27-370 v153 a

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.

10aBinding energy10acancer10aDisease10agenetic programming10amachine learning10aprotein-protein interactions10asymbolic regression1 aVyas, Renu1 aBapat, Sanket1 aGoel, Purva1 aKarthikeyan, Muthukumarasamy1 aTambe, Sanjeev, S.1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/application-genetic-programming-gp-formalism-building-disease-predictive-models-protein00591nas a2200109 4500008004100000245012200041210006900163260007400232100003300306700001500339856012700354 2018 eng d00aCCCTK (Compute Cure for Cancer ToolKit) an open source drug discovery platform for design of novel anti-cancer agents0 aCCCTK Compute Cure for Cancer ToolKit an open source drug discov aNew Orleans, LAbACS, 1155 16TH ST, NW, Washington, DC 20036 USAcMAR1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/ccctk-compute-cure-cancer-toolkit-open-source-drug-discovery-platform-design-novel-anti00512nas a2200097 4500008004100000245009700041210006900138260005400207100003300261856012000294 2018 eng d00aCCCTK: High performance molecular informatics toolkit for the design of anti-cancer molecule0 aCCCTK High performance molecular informatics toolkit for the des a Boston, MAbAmerican-Chemical-Society (ACS)cAUG1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/ccctk-high-performance-molecular-informatics-toolkit-design-anti-cancer-molecule00612nas a2200157 4500008004100000245006100041210006100102260007400163100001900237700003300256700001700289700002000306700002200326700001600348856009000364 2018 eng d00aImpact of stereochemistry on sharing chemical structures0 aImpact of stereochemistry on sharing chemical structures aNew Orleans, LAbACS, 1155 16TH ST, NW, Washington, DC 20036 USAcMAR1 aBanik, Gregory1 aKarthikeyan, Muthukumarasamy1 aNedwed, Karl1 aKunitsky, Keith1 aD'Souza, Michelle1 aAbshear, Ty uhttps://library.ncl.res.in/content/impact-stereochemistry-sharing-chemical-structures03557nas a2200157 4500008004100000245008600041210006900127260000800196300001200204490000700216520299400223100001803217700001503235700003303250856011603283 2019 eng d00aExploring energy profiles of protein-protein interactions (PPIs) Using DFT method0 aExploring energy profiles of proteinprotein interactions PPIs Us cJUN a670-6770 v163 a

Background: Large-scale energy landscape characterization of protein-protein interactions (PPIs) is important to understand the interaction mechanism and protein-protein docking methods. The experimental methods for detecting energy landscapes are tedious and the existing computational methods require longer simulation time.

Objective: The objective of the present work is to ascertain the energy profiles at the interface regions in a rapid manner to analyze the energy landscape of protein-protein interactions

Methods: The atomic coordinates obtained from the X-ray and NMR spectroscopy data are considered as inputs to compute cumulative energy profiles for experimentally validated protein-protein complexes. The energies computed by the program were comparable to the standard molecular dynamics simulations.

Results: The PPI Profiler not only enables rapid generation of energy profiles but also facilitates the detection of hot spot residue atoms involved therein.

Conclusion: The hotspot residues and their computed energies matched with the experimentally determined hot spot residues and their energies which correlated well by employing the MM/GBSA method. The proposed method can be employed to scan entire proteomes across species at an atomic level to study the key PPI interactions.

1 aBapat, Sanket1 aVyas, Renu1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/exploring-energy-profiles-protein-protein-interactions-ppis-using-dft-method01794nas a2200169 4500008004100000245007400041210006900115260003400184520117000218100002101388700001801409700001801427700002301445700001801468700003301486856010501519 2019 eng d00aLeukemia cancer detection using image analytics : (comparative study)0 aLeukemia cancer detection using image analytics comparative stud aPune, IndiabIEEE XplorecSEP3 aLeukemia 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, "Orange-Data Mining" 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.1 aBelhekar, Akshay1 aGagare, Kumar1 aBedse, Ritesh1 aBhelkar, Yugandhar1 aRajeswari, K.1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/leukemia-cancer-detection-using-image-analytics-comparative-study01227nas a2200229 4500008004100000022001400041245013800055210006900193260000800262300001400270490000600284520036100290653002500651653002800676653003300704100002300737700002200760700003100782700003300813700002400846856012700870 2019 eng d a2365-654900ap-Nitrophenylcarbonates: a new class of compounds for chemodosimetric colorimetric fluoride anion sensing detectable by the naked eye0 apNitrophenylcarbonates a new class of compounds for chemodosimet cFEB a1830-18330 v43 a

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.

10acolorimetric sensing10ap-nitrophenyl carbonate10aselective fluoride detection1 aYadav, Amit, Kumar1 aChilukuri, Harsha1 aKumari, Linthoinganbi, Raj1 aKarthikeyan, Muthukumarasamy1 aFernandes, Moneesha uhttps://library.ncl.res.in/content/p-nitrophenylcarbonates-new-class-compounds-chemodosimetric-colorimetric-fluoride-anion01951nas a2200229 4500008004100000245015100041210006900192260000800261300001200269490000700281520111000288100001801398700001801416700001601434700002901450700001901479700002201498700003301520700002201553700001501575856013101590 2019 eng d00aSynthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-chromone fused α-aminophosphonates as Src kinase inhibitors0 aSynthesis biological evaluation and molecular modeling studies o cFEB a111-1170 v783 aA 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.1 aBapat, Sanket1 aViswanadh, N.1 aMujahid, M.1 aShirazi, Amir, Nasrolahi1 aTiwari, Rakesh1 aParang, Keykavous1 aKarthikeyan, Muthukumarasamy1 aMuthukrishnan, M.1 aVyas, Renu uhttps://library.ncl.res.in/content/synthesis-biological-evaluation-and-molecular-modeling-studies-novel-chromoneaza-chromone-002158nas a2200205 4500008004100000022001400041245009900055210006900154260000800223300001200231490000800243520140900251653003101660653002001691653003001711100002201741700003301763700002601796856013001822 2019 eng d a0141-813000aZebrafish acid ceramidase: expression in Pichia pastoris GS115and biochemical characterization0 aZebrafish acid ceramidase expression in Pichia pastoris GS115and cFEB a587-5930 v1223 a

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 & 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.

10aAutoproteolytic processing10aPichia pastoris10aZebrafish acid ceramidase1 aRajput, Vijay, B.1 aKarthikeyan, Muthukumarasamy1 aRamasamy, Sureshkumar uhttps://library.ncl.res.in/content/zebrafish-acid-ceramidase-expression-pichia-pastoris-gs115and-biochemical-characterization03539nas a2200289 4500008004100000022001400041245013400055210006900189260000800258300001400266490000700280520258100287653001002868653002102878653001602899653001402915653001602929653002202945653002202967100001802989700002803007700002003035700001503055700001803070700003303088856012803121 2020 eng d a1386-207300aDesign of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants0 aDesign of novel druglike molecules using informatics rich second cJAN a1113-11310 v233 a

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.

10aDrugs10amedicinal plants10aMetabolites10ascaffolds10atext mining10avirtual libraries10avirtual screening1 aKarade, Divya1 aVijayasarathi, Durairaj1 aKadoo, Narendra1 aVyas, Renu1 aIngle, P., K.1 aKarthikeyan, Muthukumarasamy uhttps://library.ncl.res.in/content/design-novel-drug-molecules-using-informatics-rich-secondary-metabolites-analysis-indian02385nas a2200349 4500008004100000245014400041210006900185260000800254300001100262490000600273520123500279653001301514653002401527653001401551653003401565653001301599653001401612653002201626653004301648653001801691100001601709700002301725700001801748700003301766700001701799700001601816700002401832700001701856700001601873700001801889856012801907 2022 eng d00aBESFA: bioinformatics based evolutionary, structural & functional analysis of prostate, Placenta, Ovary, Testis, and Embryo (POTE) paralogs0 aBESFA bioinformatics based evolutionary structural functional an cSEP ae104760 v83 a

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.

10a(MMGBSA)10aAdaptive divergence10aEvolution10ageneralized born surface area10aHomology10aMechanics10aMolecular docking10aMolecular dynamic simulation molecular10aPOTE paralogs1 aQazi, Sahar1 aJit, Bimal, Prasad1 aDas, Abhishek1 aKarthikeyan, Muthukumarasamy1 aSaxena, Amit1 aRay, M., D.1 aSingh, Angel, Rajan1 aRaza, Khalid1 aJayaram, B.1 aSharma, Ashok uhttps://library.ncl.res.in/content/besfa-bioinformatics-based-evolutionary-structural-functional-analysis-prostate-placenta02031nas a2200253 4500008004100000022001400041245010200055210006900157260000800226300001400234490000700248520121200255653001301467653001501480653001801495653001701513653002101530653001601551100002001567700001801587700003301605700001501638856012401653 2022 eng d a1568-026600aReview on computational analysis of big data in breast cancer for predicting potential biomarkers0 aReview on computational analysis of big data in breast cancer fo cSEP a1793-18100 v223 a

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.

10aBig data10aBiomarkers10aBreast cancer10aDriver genes10aNetwork analysis10atext mining1 aShaikh, Nilofer1 aBapat, Sanket1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/review-computational-analysis-big-data-breast-cancer-predicting-potential-biomarkers02768nas a2200265 4500008004100000022001400041245013800055210006900193260000900262300001400271490000700285520182600292653002702118653002102145653002202166653003002188653004202218653002202260100002002282700002002302700000502322700003302327700001502360856012702375 2023 eng d a0739-110200aComprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets0 aComprehensive molecular docking and dynamic simulations for drug cNOV  a7735-77430 v413 a

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

10achemotherapeutic drugs10aDrug repurposing10aMolecular docking10amolecular simulation (MD)10astructure-based drug designing (SBDD)10avirtual screening1 aShaikh, Nilofer1 aLinthoi, R., K.1 a1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/comprehensive-molecular-docking-and-dynamic-simulations-drug-repurposing-clinical-drugs02482nas a2200277 4500008004100000022001400041245011000055210006900165260000800234300001200242490000700254520152400261653001801785653003101803653001301834653002601847653002001873100002301893700002401916700002001940700002701960700002901987700003302016700002402049856013102073 2025 eng d a0971-589400aTranscriptome analyses reveal TaWRKY41 as a potential candidate governing spot blotch resistance in wheat0 aTranscriptome analyses reveal TaWRKY41 as a potential candidate  cAPR a591-6080 v313 a

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.

10aPlant defense10aplant-pathogen interaction10aTaWRKY4110aTranscription factors10aTranscriptomics1 aDayama, Bhakti, R.1 aMahadik, Varsha, A.1 aSomani, Deepika1 aShinde, Balkrishna, A.1 aKondhare, Kirtikumar, R.1 aKarthikeyan, Muthukumarasamy1 aKadoo, Narendra, Y. uhttps://library.ncl.res.in/content/transcriptome-analyses-reveal-tawrky41-potential-candidate-governing-spot-blotch-resistance