Use of 3D QSAR models for database screening: a feasibility study

Abstract

The applicability and scope of 3D QSAR methods (CoMFA, CoMSIA) to screen databases are examined. A protocol requiring minimal user intervention has been established to align training and test set molecules using FlexS. As model system isozymes of human carbonic anhydrase (hCA) are used, all results are exemplified studying affinity toward hCA II and selectivity between hCA I and II. The predictive power of the obtained models is assessed through prediction of 663 compounds not included in the training set and compared to 2D QSAR models derived from fragment (MACCS) or property (VSA) based descriptors. The predictive power is evaluated with respect to the following criteria: the numerical, concerning the absolute accuracy of prediction, and the categorical, characterizing the ability to assign a compound to the correct activity class.