Visualization and Chemical Interpretation of Multi-Target Structure-Activity Relationships Using SOMPLS

Abstract

In quantitative structure-activity relationships (QSAR), partial least squares (PLS) are of particular interest as a statistical method. Since successful applications of PLS to QSAR data set, PLS has evolved for coping with more demands associated with complex data structures. Especially, PLS variants focusing on visualization and chemical interpretation are highly desirable in modeling multi-target structure-activity relationships. In this paper, we employed the self-organized PLS (SOMPLS) approach to predict multiple inhibitory activities against three serine protease receptors (Thrombin, Trypsin and Factor Xa). Volsurf descriptors were used as chemical descriptors. From the SOMPLS analysis, we could catch rough trends about what chemical features are essential to each serine protease protein. Their chemical features could be successfully validated from X-ray crystal structures and the corresponding alignment residues.