Effect of steric molecular field settings on CoMFA predictivity

Abstract

Steric molecular field can be represented in a number of ways in comparative molecular field analysis (CoMFA). This study aimed to investigate whether the choice of steric molecular field settings significantly influences the predictive performance of CoMFA and, if so, which is the best. The three-dimensional quantitative structure activity relationship (3D-QSAR) models based on Lennard–Jones, indicator, parabolic and Gaussian steric fields were compared using 28 datasets taken from the literature. The analysis of the predictive ability of these models (cross validated R2) indicates that steric fields in which the value drops off quickly with distance (i.e. Lennard–Jones and indicator fields) tend to perform better than the Gaussian version, which has a slower and smoother decrease. Furthermore, depending on the steric field type used, the field sampling density (i.e. grid spacing) has a variable influence on the predictive ability of the models generated.