Enhancement of ordinal CoMFA by ridge logistic partial least squares

Conventional comparative molecular field analysis (CoMFA) requires at least 3 orders of experimental data, such as IC 50 and K i, to obtain a good model, although practically there are many screening assays where biological activity is measured only by rating scale. To improve three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis, we developed in this study a modified ordinal classification-oriented CoMFA using partial-least-squares generalized linear regression and ridge estimation. The modified Logistic CoMFA was validated using a corticosteroid binding globulin receptor binding data set, a benchmark for 3D-QSAR, and an acetylcholine esterase inhibitor data set. Our results show that modification of Logistic CoMFA enhanced both prediction accuracy and 3D graphical analysis. In addition, the 3D graphical analysis of the modified Logistic CoMFA was much improved. This improvement resulted in more accurate information on the binding mode between proteins and ligands than in the case of conventional CoMFA.