Improved 3D-QSAR prediction by multiple-conformational alignment: A case study on PTP1B inhibitors. (December 2019) - Zhang, Xiangyu; Mao, Jianping; Li, Wei; Koike, Kazuo; Wang, Jian
Graphical abstract: Highlights: A solution to optimize QSAR prediction by multiple-conformational alignment was proposed, with PTP1B inhibitors as case study. The alignment methodologies used here would be useful for constructing accurate 3D-QSAR model for various disease targets. The QSAR analysis could greatly help us to understand essential structural features of inhibitors required by its target. 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 (r 2 ) and cross-validated squared correlation coefficient (q 2 ) of comparative molecular field analysis (CoMFA) were 0.992 and 0.694; the r 2 and q 2 of comparative molecular similarity indices analysis (CoMSIA) were 0.972 and 0.603, respectively. The alignment methodologies used here not only generated a robust QSAR model with useful molecular field contour maps for designing novel PTP1B inhibitors, but also provided a solution for constructing accurate 3D-QSAR model for various disease targets. Undoubtedly, such attempt in QSAR analysis would greatly help us to understand essential structural features of inhibitors required by its target, and so as to discover more promising chemical derivatives.