Three-dimensional classification structure–activity relationship analysis using convolutional neural network

Abstract

Quantitative structure–activity relationship (QSAR) techniques, especially those that possess three-dimensional attributes, such as the comparative molecular field analysis (CoMFA), are frequently used in modern-day drug design and other related research domains. However, the requirement for accurate alignment of compounds in CoMFA increases the difficulties encountered in its use. This has led to the development of several techniques—such as VolSurf, Grid-independent descriptors (GRIND), and Anchor-GRIND—which do not require such an alignment. We propose a technique to construct the prediction model that uses molecular interaction field grid potentials as inputs to convolutional neural network. The proposed model has been found to demonstrate higher accuracy compared to the conventional descriptor-based QSAR models as well as Anchor-GRIND techniques. In addition, the method is target independent, and is capable of providing useful information regarding the importance of individual atoms constituting the compounds contained in the chemical dataset used in the proposed analysis. In view of these advantages, the proposed technique is expected to find wide applications in future drug-design operations.