@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 = {<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>},
	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.}
}
