02142nas 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<p>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.</p>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-003061nas a2200265   4500008004100000022001400041245012400055210006900179260007300248300001100321490000900332520205300341653002802394653002302422653002402445653002702469653002602496653005102522100001602573700001802589700001502607700001802622700002302640856013202663       2015                        eng d  a0021-967300aGenetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices0 aGenetic programming based quantitative structureretention relati  aPO BOX 211, 1000 AE AMSTERDAM, NETHERLANDSbELSEVIER SCIENCE BVcNOV  a98-1090 v14203 a<p>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 (&gt;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.</p>10aArtificial intelligence10aGas chromatography10agenetic programming10aKovats retention index10aMolecular descriptors10aQuantitative structure-retention relationships1 aGoel, Purva1 aBapat, Sanket1 aVyas, Renu1 aTambe, Amruta1 aTambe, Sanjeev, S.  uhttps://library.ncl.res.in/content/genetic-programming-based-quantitative-structure-retention-relationships-prediction-kovats-002881nas 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<p>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.</p>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-support02246nas 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<p>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.</p>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-protein01472nas a2200217   4500008004100000245009700041210006900138260000800207300002700215490000600242520073600248100002100984700001801005700001801023700001501041700002001056700001901076700001501095700002201110856012201132       2018                        eng d00aIdentification of potent chromone embedded [1,2,3]-triazoles as novel anti-tubercular agents0 aIdentification of potent chromone embedded 123triazoles as novel  cAPR  aArticle Number: 1717500 v53 aA 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.1 aNalla, Viswanadh1 aShaikh, Aslam1 aBapat, Sanket1 aVyas, Renu1 aKarthikeyan, M.1 aYogeeswari, P.1 aSriram, D.1 aMuthukrishnan, M.  uhttps://library.ncl.res.in/content/identification-potent-chromone-embedded-123-triazoles-novel-anti-tubercular-agents03557nas 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<p>Background: Large-scale <span class="hitHilite">energy</span> landscape characterization <span class="hitHilite">of</span> <span class="hitHilite">protein-protein</span> <span class="hitHilite">interactions</span> (<span class="hitHilite">PPIs</span>) is important <span class="hitHilite">to</span> understand <span class="hitHilite">the</span> interaction mechanism and <span class="hitHilite">protein-protein</span> docking methods. <span class="hitHilite">The</span> experimental methods for detecting <span class="hitHilite">energy</span> landscapes are tedious and <span class="hitHilite">the</span> existing computational methods require longer simulation time.<br />
	<br />
	Objective: <span class="hitHilite">The</span> objective <span class="hitHilite">of</span> <span class="hitHilite">the</span> present work is <span class="hitHilite">to</span> ascertain <span class="hitHilite">the</span> <span class="hitHilite">energy</span> <span class="hitHilite">profiles</span> at <span class="hitHilite">the</span> interface regions in <span class="hitHilite">a</span> rapid manner <span class="hitHilite">to</span> analyze <span class="hitHilite">the</span> <span class="hitHilite">energy</span> landscape <span class="hitHilite">of</span> <span class="hitHilite">protein-protein</span> <span class="hitHilite">interactions</span><br />
	<br />
	Methods: <span class="hitHilite">The</span> atomic coordinates obtained from <span class="hitHilite">the</span> X-ray and NMR spectroscopy data are considered as inputs <span class="hitHilite">to</span> compute cumulative <span class="hitHilite">energy</span> <span class="hitHilite">profiles</span> for experimentally validated <span class="hitHilite">protein-protein</span> complexes. <span class="hitHilite">The</span> energies computed by <span class="hitHilite">the</span> program were comparable <span class="hitHilite">to</span> <span class="hitHilite">the</span> standard molecular dynamics simulations.<br />
	<br />
	Results: <span class="hitHilite">The</span> PPI Profiler not only enables rapid generation <span class="hitHilite">of</span> <span class="hitHilite">energy</span> <span class="hitHilite">profiles</span> but also facilitates <span class="hitHilite">the</span> detection <span class="hitHilite">of</span> hot spot residue atoms involved therein.<br />
	<br />
	Conclusion: <span class="hitHilite">The</span> hotspot residues and their computed energies matched with <span class="hitHilite">the</span> experimentally determined hot spot residues and their energies which correlated well by employing <span class="hitHilite">the</span> MM/GBSA <span class="hitHilite">method</span>. <span class="hitHilite">The</span> proposed <span class="hitHilite">method</span> can be employed <span class="hitHilite">to</span> scan entire proteomes across species at an atomic level <span class="hitHilite">to</span> study <span class="hitHilite">the</span> key PPI <span class="hitHilite">interactions</span>.</p>
1 aBapat, Sanket1 aVyas, Renu1 aKarthikeyan, Muthukumarasamy  uhttps://library.ncl.res.in/content/exploring-energy-profiles-protein-protein-interactions-ppis-using-dft-method01951nas 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-002031nas 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<p>
	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.</p>
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-biomarkers