How to Generate Reliable and Predictive CoMFA Models

Abstract

Comparative Molecular Field Analysis (CoMFA) is a mainstream and down-to-earth 3D QSAR technique in the coverage of drug discovery and development. Even though CoMFA is remarkable for high predictive capacity, the intrinsic data-dependent characteristic still makes this methodology certainly be handicapped by noise. It's well known that the default settings in CoMFA can bring about predictive QSAR models, in the meanwhile optimized parameters was proven to provide more predictive results. Accordingly, so far numerous endeavors have been accomplished to ameliorate the CoMFA model's robustness and predictive accuracy by considering various factors, including molecular conformation and alignment, field descriptors and grid spacing. Herein, we would like to make a comprehensive survey of the conceivable descriptors and their contribution to the CoMFA model's predictive ability.