NDLI: QMOD: physically meaningful QSAR

Content Provider	SpringerLink
Author	Jain, Ajay N.
Copyright Year	2010
Abstract	Computational methods for predicting ligand affinity where no protein structure is known generally take the form of regression analysis based on molecular features that have only a tangential relationship to a protein/ligand binding event. Such methods have utility in retrospective rationalization of activity patterns of substituents on a common scaffold, but are limited when either multiple scaffolds are present or when ligand alignment varies significantly based on structural changes. In addition, such methods generally assume independence and additivity of effect from scaffold substituents. Collectively, these non-physical modeling assumptions sharply limit the utility of widely used QSAR approaches for prospective prediction of ligand activity. The recently introduced Surflex-QMOD approach, by virtue of constructing physical models of binding sites, comes closer to a modeling approach that is congruent with protein ligand binding events. A set of congeneric CDK2 inhibitors showed that induced binding pockets can be quite congruent with the enzyme’s active site but that model predictivity within a chemical series does not necessarily depend on congruence. Muscarinic antagonists were used to show that the QMOD approach is capable of making accurate predictions in cases where highly non-additive structure activity effects exist. The QMOD method offers a means to go beyond non-causative correlations in QSAR analysis.
Starting Page	865
Ending Page	878
Page Count	14
File Format	PDF
ISSN	0920654X
Journal	Journal of Computer-Aided Molecular Design
Volume Number	24
Issue Number	10
e-ISSN	15734951
Language	English
Publisher	Springer Netherlands
Publisher Date	2010-08-19
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	QSAR Ligand based modeling Similarity Docking Computer Applications in Chemistry Animal Anatomy / Morphology / Histology Physical Chemistry
Content Type	Text
Resource Type	Article
Subject	Drug Discovery Physical and Theoretical Chemistry Computer Science Applications

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