Conformational fingerprints in the modelling performance of MIA-QSAR: a case for SARS-CoV protease inhibitors

Abstract

Multivariate image analysis applied to quantitative structure–activity relationships (MIA-QSAR) has proved to be a high-performance 2D tool for drug design purposes. Nonetheless, MIA-QSAR strategy does not efficiently incorporate conformational information. Therefore, understanding the implications of including this type of data into the MIA-QSAR model, in terms of predictability and interpretability, seems a crucial task. Conformational information was included considering the optimised geometries and the docked structures of a series of disulfide compounds potentially useful as SARS-CoV protease inhibitors. The traditional analysis (based on flat-shape molecules) proved itself as the most effective technique, which means that, despite the undeniable importance of conformation for biomolecular behaviour, this type of information did not bring relevant contributions for MIA-QSAR modelling. Consequently, promising drug candidates were proposed on the basis of MIA-plot analyses, which account for PLS regression coefficients and variable importance in projection scores of the MIA-QSAR model.