02881nas 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-001951nas 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-0