@article { ISI:000360934800002,
	title = {Pharmacophore and docking based virtual screening of validated mycobacterium tuberculosis targets},
	journal = {Combinatorial Chemistry \& High Throughput Screening},
	volume = {18},
	number = {7},
	year = {2015},
	month = {AUG},
	pages = {624-637},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>Target based virtual screening has surpassed ligand based virtual screening methods in the recent past mainly as it provides more clues regarding intermolecular interactions and takes into consideration the flexible receptor as well. The current methodology describes a computational strategy of predicting Mycobacterium tuberculosis (M. tuberculosis) binders for five well studied targets representing M. tuberculosis proteome encompassing most of the known mechanisms of action. The diversity of the targets was affirmed by their active site analysis and structural studies. The current approach employed pharmacophore searching, docking and clustering techniques in tandem and was validated by enrichment studies using the available Schrodinger data set consisting of 1000 decoys. The application of this methodology was demonstrated by predicting potential molecular targets for fifty newly synthesized compounds. Cross docking studies on the targets were carried out with 4512 known inhibitors utilizing a high performance computing platform to reveal underlying affinity and promiscuity patterns. Optimum binding energy range for all targets as determined by high throughput docking was found to be -3 to -13 kcal/mol.</p>},
	keywords = {Binding energy, docking, Mycobacterium tuberculosis, open source drug discovery (OSDD), pharmacophore, structure based drug design (SBDD)},
	issn = {1386-2073},
	doi = {10.2174/1386207318666150703112759},
	author = {Vyas, Renu and Karthikeyan, Muthukumarasamy and Nainaru, Ganesh and Muthukrishnan, Murugan}
}
@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 = {<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>},
	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.}
}
