@article { ISI:000360934400007, title = {Design and development of cheminfocloud: an integrated cloud enabled platform for virtual screening}, journal = {Combinatorial Chemistry \& High Throughput Screening}, volume = {18}, number = {6}, year = {2015}, month = {JAN}, pages = {604-619}, publisher = {BENTHAM SCIENCE PUBL LTD}, address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES}, abstract = {

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}, issn = {1386-2073}, doi = {10.2174/1386207318666150703113656}, author = {Karthikeyan, Muthukumarasamy and Pandit, Deepak and Bhavasar, Arvind and Vyas, Renu} } @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 {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.} }