A group center overlap based approach for "3D QSAR" studies on TIBO derivatives.

摘要: Current challenges in drug designing and lead optimization has reached a bottle neck where the main onus lies on rigorous validation to afford robust predictive models. In present study, we have suggested that structure-activity relationship (SAR) models based statistical analyses can serve as effective screening tools for large volume of compounds either chemical databases or virtual libraries. 3D descriptors derived from similarity-based alignment molecules with respect group center overlap each individual template point other “alignment averaged,” but significant (ClogP, molar refractivity, connolly accessible area) were used generate QSAR The results indicated artificial neural network method (r2 = 0.902) proved be superior multiple linear regression 0.810). Cross an external set was reasonably satisfactory. Screening PubChem compound database obtained, yielded 14 newer modified belonging TIBO class inhibitors, well as, two novel scaffolds, enhanced binding efficacy hits. These hits may targeted toward potent lead-optimization help synthesizing new potential therapeutic utility. © 2008 Wiley Periodicals, Inc. J Comput Chem, 2009