@article { ISI:000360934800005, title = {Chemoinformatics approach for building molecular networks from marine organisms}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {7}, year = {2015}, month = {JAN}, pages = {673-684}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

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}, issn = {1386-2073}, doi = {10.2174/1386207318666150703112950}, author = {Karthikeyan, Muthukumarasamy and Nimje, Deepika and Pahujani, Rakhi and Tyagi, Kushal and Bapat, Sanket and Vyas, Renu and Padmakumar, Krishna Pillai} } @article { ISI:000364264800011, title = {Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices}, journal = {Journal of Chromatography A}, volume = {1420}, year = {2015}, month = {NOV}, pages = {98-109}, publisher = {ELSEVIER SCIENCE BV}, address = {PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS}, abstract = {

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}, issn = {0021-9673}, doi = {10.1016/j.chroma.2015.09.086}, author = {Goel, Purva and Bapat, Sanket and Vyas, Renu and Tambe, Amruta and Tambe, Sanjeev S.} } @article { ISI:000360934800004, title = {Study of applications of machine learning based classification methods for virtual screening of lead molecules}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {7}, year = {2015}, month = {AUG}, pages = {658-672}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

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}, issn = {1386-2073}, doi = {10.2174/1386207318666150703112447}, author = {Vyas, Renu and Bapat, Sanket and Jain, Esha and Tambe, Sanjeev S. and Karthikeyan, Muthukumarasamy and Kulkarni, Bhaskar D.} } @article {43410, title = {Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis}, journal = {Computational Biology and Chemistry}, volume = {65}, year = {2016}, month = {DEC}, pages = {37-44}, abstract = {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.}, doi = {10.1016/j.compbiolchem.2016.09.011}, author = {Vyas, Renu and Bapat, Sanket and Jain, Esha and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev and Kulkarni, Bhaskar D.} } @article {44229, title = {Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data}, journal = {IEEE-ACM Transactions on Computational Biology and Bioinformatics}, volume = {15}, year = {2018}, month = {FEB}, pages = {27-37}, type = {Article}, abstract = {

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}, issn = {1545-5963}, doi = {10.1109/TCBB.2016.2621042}, author = {Vyas, Renu and Bapat, Sanket and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.} } @article {44497, title = {Identification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents}, journal = {Royal Society Open Science}, volume = {5}, year = {2018}, month = {APR}, pages = {Article Number: 171750}, abstract = {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.}, doi = {10.1098/rsos.171750}, author = {Nalla, Viswanadh and Shaikh, Aslam and Bapat, Sanket and Vyas, Renu and Karthikeyan, M. and Yogeeswari, P. and Sriram, D. and Muthukrishnan, M.} } @article {46225, title = {Exploring energy profiles of protein-protein interactions (PPIs) Using DFT method}, journal = {Letters in Drug Design \& Discovery}, volume = {16}, year = {2019}, month = {JUN}, pages = {670-677}, type = {Article}, abstract = {

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.

}, doi = {10.2174/1570180815666180815151141}, author = {Bapat, Sanket and Vyas, Renu and Karthikeyan, Muthukumarasamy} } @article {47493, title = {Synthesis, biological evaluation and molecular modeling studies of novel chromone/Aza-chromone fused α-aminophosphonates as Src kinase inhibitors}, journal = {Journal of Scientific and Industrial Research}, volume = {78}, year = {2019}, month = {FEB}, pages = {111-117}, type = {Article}, abstract = {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 {\textmu}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.}, author = {Bapat, Sanket and Viswanadh, N. and Mujahid, M. and Shirazi, Amir Nasrolahi and Tiwari, Rakesh and Parang, Keykavous and Karthikeyan, Muthukumarasamy and Muthukrishnan, M. and Vyas, Renu} } @article {48103, title = {Review on computational analysis of big data in breast cancer for predicting potential biomarkers}, journal = {Current Topics in Medicinal Chemistry}, volume = {22}, year = {2022}, month = {SEP}, pages = {1793-1810}, type = {Review}, abstract = {

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}, issn = {1568-0266}, doi = {10.2174/1568026622666220907121942}, author = {Shaikh, Nilofer and Bapat, Sanket and Karthikeyan, Muthukumarasamy and Vyas, Renu} }