Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors.

Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors. Comput Biol Chem. 2019 Oct 01;83:107134 Authors: Zhang X, Mao J, Li W, Koike K, Wang J Abstract Three-dimension quantitative structure activity relationship (3D-QSAR) was one of the major statistical techniques to investigate the correlation of biological activity with structural properties of candidate molecules, and the accuracy of statistic greatly depended on molecular alignment methodology. Exhaustive conformational search and successful conformational superposition could extremely improve the predictive accuracy of QSAR modeling. In this work, we proposed a solution to optimize QSAR prediction by multiple-conformational alignment methods, with a set of 40 flexible PTP1B inhibitors as case study. Three different molecular alignment methods were used for the development of 3D-QSAR models listed as following: (1) docking-based alignment (DBA); (2) pharmacophore-based alignment (PBA) and (3) co-crystallized conformer-based alignment (CCBA). Among these three alignments, it was indicated that the CCBA was the best and the fastest strategy in 3D-QSAR development, with the square correlation coefficient (r2) and cross-validated squared correlation coefficient (q2) of comparative molecular field analysis (CoMFA) were 0.992 and 0.694; the r2 and q2 of comparative molecular similarity indices analysis (CoMSIA) were 0.972 and 0.603, respect...
Source: Computational Biology and Chemistry - Category: Bioinformatics Authors: Tags: Comput Biol Chem Source Type: research