Advanced PLS Technique Focusing on Visualization and Chemical Interpretation - SOMPLS Analysis of Serine Protease Inhibitors -

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 for molecular design. In this paper, we employed the self-organized map PLS (SOMPLS) approach to predict multiple inhibitory activities against three serine protease receptors (Factor Xa, Tryptase and urokinase-type Plasminogen Activator (uPA)). Retrosynthetic Combinatorial Analysis Procedure (RECAP) fingerprints were used as chemical descriptors that express the existence of specific substructure in the molecule. From the SOMPLS analysis and the subsequent correlation map, essential fragments for each serine protease were easily identified. From the correlation map, we designed best combinations of fragments at each substituent position for each serine protease protein. The essential fragments could be validated from X-ray crystal structures of serine protease receptors in computer graphics. SOMPLS is an unique approach that makes data-mining feasible from visualization of structure-activity data biased to ligand-based view point.