Comparing Predictive Ability of QSAR/QSPR Models Using 2D and 3D Molecular Representations

Abstract

Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models predict biological activity and molecular property based on the numerical relationship between chemical structures and activity (property) values. Topological information of molecular structures is usually utilized for this purpose (2D representations, 2D descriptors). However, conformational information seems important because molecules are in the three-dimensional space. As a three-dimensional molecular representation(3D descriptors) applicable to diverse compounds, similarity between a test molecule and a set of reference molecules has been previously proposed. In this study, we introduced the 3D descriptors into QSAR/QSPR modeling (regression tasks). Furthermore, we investigated relative merits of 3D descriptors over 2D in terms of the diversity of training and test data sets. For the prediction task of quantum mechanics-based properties, the 3D descriptors were superior to 2D. For predicting activity of small molecules against specific biological targets, no consistent trend was observed in the difference of performance using the two types of representations, irrespective of the diversity of training and test data sets.