@article { ISI:000266525900010,
	title = {Review on lazy learning regressors and their applications in QSAR},
	journal = {Combinatorial Chemistry \& High Throughput Screening},
	volume = {12},
	number = {4},
	year = {2009},
	month = {MAY},
	pages = {440-450},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>Building accurate quantitative structure-activity relationships (QSAR) is important in drug design, environmental modeling, toxicology, and chemical property prediction. QSAR methods can be utilized to solve mainly two types of problems viz., pattern recognition, (or classification) where output is discrete (i.e. class information), e. g., active or non-active molecule, binding or non-binding molecule etc., and function approximation, (i.e. regression) where the output is continuous (e.g., actual activity prediction). The present review deals with the second type of problem (regression) with specific attention to one of the most effective machine learning procedures, viz. lazy learning. The methodologies of the algorithm along with the relevant technical information are discussed in detail. We also present three real life case studies to briefly outline the typical characteristics of the modeling formalism.</p>},
	keywords = {classification, lazy learning, machine learning, Quantitative structure activity relationship (QSAR), regression},
	issn = {1386-2073},
	author = {Kulkarni, Abhijit J. and Jayaraman, Valadi K. and Kulkarni, Bhaskar D.}
}
@article { ISI:000360934800003,
	title = {Role of chemical reactivity and transition state modeling for virtual screening},
	journal = {Combinatorial Chemistry \& High Throughput Screening},
	volume = {18},
	number = {7},
	year = {2015},
	month = {JAN},
	pages = {638-657},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product-like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75\% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.</p>},
	keywords = {fingerprints, intermediates, metabolic pathways, product-like score, reactant-like score, reaction, screening, synthesis, virtual screening},
	issn = {1386-2073},
	doi = {10.2174/1386207318666150703113135},
	author = {Karthikeyan, Muthukumarasamy and Vyas, Renu and Tambe, Sanjeev S. and Radhamohan, Deepthi and Kulkarni, Bhaskar D.}
}
