Prediction models for agonists and antagonists of molecular initiation events for toxicity pathways using an improved deep-learning-based quantitative structure–activity relationship system

Abstract

In silico approaches have been studied intensively to assess the toxicological risk of vari-ous chemical compounds as alternatives to traditional in vivo animal tests. Among these ap-proaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural infor-mation. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperpa-rameters. In the present study, to achieve high throughput in the DeepSnap-DL system–which con-sists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical cal-culations—we propose an improved DeepSnap-DL approach. Using this improved system, we con-structed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.