Chemoinformatics approach for the design and screening of focused virtual libraries - Karthikeyan, Muthukumarasamy; Vyas, Renu (Practical Chemoinformatics, 2014)
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.

Machine learning methods in chemoinformatics for drug discovery - Karthikeyan, Muthukumarasamy; Vyas, Renu (Practical Chemoinformatics, 2014)
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

Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method - Vyas, Renu; Goel, Purva; Karthikeyan, Muthukumarasamy; Tambe, S. S.; Kulkarni, B. D. (Letters in Drug Design & Discovery, 2014)
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.
Keywords: ADME modeling; Caco-2 cell permeability; genetic programming; MLP; SVR

Chemoinformatics approach for building molecular networks from marine organisms - Karthikeyan, Muthukumarasamy; Nimje, Deepika; Pahujani, Rakhi; Tyagi, Kushal; Bapat, Sanket; Vyas, Renu; Padmakumar, Krishna Pillai (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Chemoinformatics; Drug design; marine; virtual library

ChemScreener: a distributed computing tool for scaffold based virtual screening - Karthikeyan, Muthukumarasamy; Pandit, Deepak; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Scaffold extraction; therapeutic category; virtual library generation; virtual screening

Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening - Karthikeyan, Muthukumarasamy; Pandit, Deepak; Bhavasar, Arvind; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Chemoinformatics; cloud computing; machine learning; Molecular docking; OpenVz; sequence alignment; spectra prediction; text mining; virtual screening

Design and development of new chemoinformatics tools for virtual screening - Karthikeyan, Muthukumarasamy; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices - Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S. (Journal of Chromatography A, 2015)
The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R-2) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. (C) 2015 Elsevier B.V. All rights reserved.
Keywords: Artificial intelligence; Gas chromatography; genetic programming; Kovats retention index; Molecular descriptors; Quantitative structure-retention relationships

MegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment - Karthikeyan, Muthukumarasamy; Pandit, Yogesh; Pandit, Deepak; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Chemoinformatics; cloud computing; malaria; text mining; virtual screening

Pharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets - Vyas, Renu; Karthikeyan, Muthukumarasamy; Nainaru, Ganesh; Muthukrishnan, Murugan (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Binding energy; docking; Mycobacterium tuberculosis; open source drug discovery (OSDD); pharmacophore; structure based drug design (SBDD)

Prediction of bioactive compounds using computed NMR chemical shifts - Karthikeyan, Muthukumarasamy; Rajamohanan, Pattuparambil Ramanpillai; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Chemical shift; fingerprints; NMR; similarity searching; virtual screening

Protein ligand complex guided approach for virtual screening - Karthikeyan, Muthukumarasamy; Pandit, Deepak; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Complexes; ligand; protein; scaffolds; sequences; similarity score; virtual screening

Role of chemical reactivity and transition state modeling for virtual screening - Karthikeyan, Muthukumarasamy; Vyas, Renu; Tambe, Sanjeev S.; Radhamohan, Deepthi; Kulkarni, Bhaskar D. (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: fingerprints; intermediates; metabolic pathways; product-like score; reactant-like score; reaction; screening; synthesis; virtual screening

Role of data and methods in chemoinformatics for virtual screening - Karthikeyan, Muthukumarasamy; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)

Role of open source tools and resources in virtual screening for drug discovery - Karthikeyan, Muthukumarasamy; Vyas, Renu (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Chemoinformatics; kinases; open source; scaffold; virtual screening

Spirochromone-chalcone conjugates as antitubercular agents: synthesis, bio evaluation and molecular modeling studies - Mujahid, Mohammad; Perumal, Yogeeswari; Sriram, D.; Basavanag, U. M. V.; Dıaz-Cervantes, Erik; Cordoba-Bahena, Luis; Robles, Juvencio; Gonnade, Rajesh Ghanshyam; Karthikeyan, Muthukumarasamy; Vyas, Renu; Krishnan, M. Muthu (RSC Advances, 2015)
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.

Study of applications of machine learning based classification methods for virtual screening of lead molecules - Vyas, Renu; Bapat, Sanket; Jain, Esha; Tambe, Sanjeev S.; Karthikeyan, Muthukumarasamy; Kulkarni, Bhaskar D. (Combinatorial Chemistry & High Throughput Screening, 2015)
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.
Keywords: Anti-anginal; anti-arrythmic; anti-bacterial; anti-convulsant; anti-depressant anti-diabetic; binary QSAR; chemophore; machine learning; pharmacophore; toxicophore

Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis - Vyas, Renu; Bapat, Sanket; Jain, Esha; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev; Kulkarni, Bhaskar D. (Computational Biology and Chemistry, 2016)
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.

ChemEngine: harvesting 3D chemical structures of supplementary data from PDF files - Karthikeyan, Muthukumarasamy; Vyas, Renu (Journal of Cheminformatics, 2016)
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.

Prediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models - Shrinivas, K.; Kulkarni, Rahul P.; Shaikh, Saif; Ghorpade, Ravindra V.; Vyas, Renu; Tambe, Sanjeev S.; Ponrathnam, S.; Kulkarni, Bhaskar D. (International Journal of Chemical Reactor Engineering, 2016)
The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as ``genetic programming (GP)'' that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.
Keywords: Alfrey-Price scheme; free radical copolymerization; genetic programming; reactivity ratio; symbolic regression

Biomedical literature mining for protein-protein interactions analysis using electronic mailing system - Karthikeyan, Muthukumarasamy; Vyas, Renu (253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes, 2017)

VIMAL: A chemoinformatics toolkit for design of novel antiviral agents through textmining of scientific literature - Karthikeyan, Muthukumarasamy; Pahujani, Rakhi; Vyas, Renu (253rd National Meeting of the American-Chemical-Society (ACS) on Advanced Materials, Technologies, Systems, and Processes, 2017)

Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data - Vyas, Renu; Bapat, Sanket; Goel, Purva; Karthikeyan, Muthukumarasamy; Tambe, Sanjeev S.; Kulkarni, Bhaskar D. (IEEE-ACM Transactions on Computational Biology and Bioinformatics, 2018)
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.
Keywords: Binding energy; cancer; Disease; genetic programming; machine learning; protein-protein interactions; symbolic regression

CCCTK (Compute Cure for Cancer ToolKit) an open source drug discovery platform for design of novel anti-cancer agents - Karthikeyan, Muthukumarasamy; Vyas, Renu (255th National Meeting and Exposition of the American-Chemical-Society (ACS) - Nexus of Food, Energy, and Water, 2018)

Identification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents - Nalla, Viswanadh; Shaikh, Aslam; Bapat, Sanket; Vyas, Renu; Karthikeyan, M.; Yogeeswari, P.; Sriram, D.; Muthukrishnan, M. (Royal Society Open Science, 2018)
A series of 20 novel chromone embedded [1,2,3]-triazoles derivatives were synthesized via an easy and convenient synthetic procedure starting from 2-hydroxy acetophenone. The in vitro anti-mycobacterial evaluation studies carried out in this work reveal that seven compounds exhibit significant inhibition against Mycobacterium tuberculosis H37Rv strain with MIC in the range of 1.56-12.5 mu g ml(-1). Noticeably, compound 6s was the most potent compound in vitro with a MIC value of 1.56 mu g ml(-1). Molecular docking and chemoinformatics studies revealed that compound 6s displayed drug-like properties against the enoyl-acyl carrier protein reductase of M. tuberculosis further establishing its potential as a potent inhibitor.

Exploring energy profiles of protein-protein interactions (PPIs) Using DFT method - Bapat, Sanket; Vyas, Renu; Karthikeyan, Muthukumarasamy (Letters in Drug Design & Discovery, 2019)
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.

Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-Chromone fused alpha-aminophosphonates as src kinase inhibitors - Bapat, S.; Viswanadh, N.; Mujahid, M.; Shirazi, A. N.; Tiwari, R. K.; Parang, K.; Karthikeyan, M.; Muthukrishnan, M.; Vyas, Renu (Journal of Scientific & Industrial Research, 2019)
A series of novel chromone/aza-chromone fused alpha-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 mu M. 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.

Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-chromone fused α-aminophosphonates as Src kinase inhibitors - Bapat, Sanket; Viswanadh, N.; Mujahid, M.; Shirazi, Amir Nasrolahi; Tiwari, Rakesh; Parang, Keykavous; Karthikeyan, Muthukumarasamy; Muthukrishnan, M.; Vyas, Renu (Journal of Scientific and Industrial Research, 2019)
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.

Design of novel drug-like molecules using informatics rich secondary metabolites analysis of Indian medicinal and aromatic plants - Karade, Divya; Vijayasarathi, Durairaj; Kadoo, Narendra; Vyas, Renu; Ingle, P. K.; Karthikeyan, Muthukumarasamy (Combinatorial Chemistry & High Throughput Screening, 2020)
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.
Keywords: Drugs; medicinal plants; Metabolites; scaffolds; text mining; virtual libraries; virtual screening

Review on computational analysis of big data in breast cancer for predicting potential biomarkers - Shaikh, Nilofer; Bapat, Sanket; Karthikeyan, Muthukumarasamy; Vyas, Renu (Current Topics in Medicinal Chemistry, 2022)
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.
Keywords: Big data; Biomarkers; Breast cancer; Driver genes; Network analysis; text mining

Comprehensive molecular docking and dynamic simulations for drug repurposing of clinical drugs against multiple cancer kinase targets - Shaikh, Nilofer; Linthoi, R. K.; Swamy, V, K.; Karthikeyan, Muthukumarasamy; Vyas, Renu (Journal of Biomolecular Structure and Dynamics, 2023)
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
Keywords: chemotherapeutic drugs; Drug repurposing; Molecular docking; molecular simulation (MD); structure-based drug designing (SBDD); virtual screening