01794nas a2200229   4500008004100000022001400041245007000055210006900125260011100194300001200305490000700317520093200324653001901256653001801275653002101293653005601314653001501370100002601385700002601411700002601437856010101463       2009                        eng d  a1386-207300aReview on lazy learning regressors and their applications in QSAR0 aReview on lazy learning regressors and their applications in QSA  aEXECUTIVE STE Y26, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcMAY  a440-4500 v123 a<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>10aclassification10alazy learning10amachine learning10aQuantitative structure activity relationship (QSAR)10aregression1 aKulkarni, Abhijit, J.1 aJayaraman, Valadi, K.1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/review-lazy-learning-regressors-and-their-applications-qsar-002540nas a2200301   4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520149700338653001701835653001801852653002301870653002301893653002401916653001301940653001401953653001401967653002201981100003302003700001502036700002302051700002402074700002602098856011402124       2015                        eng d  a1386-207300aRole of chemical reactivity and transition state modeling for virtual screening0 aRole of chemical reactivity and transition state modeling for vi  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a638-6570 v183 a<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>10afingerprints10aintermediates10ametabolic pathways10aproduct-like score10areactant-like score10areaction10ascreening10asynthesis10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu1 aTambe, Sanjeev, S.1 aRadhamohan, Deepthi1 aKulkarni, Bhaskar, D.  uhttps://library.ncl.res.in/content/role-chemical-reactivity-and-transition-state-modeling-virtual-screening-0