ToxMPNN: A deep learning model for small molecule toxicity prediction

This study presents a toxicity predictive model, ToxMPNN, based on the message passing neural network (MPNN) architecture, aiming to predict the toxicity of small molecules. The results demonstrate that ToxMPNN outperforms other models in capturing toxic features within the molecular structure, resulting in more precise predictions with the ROC_AUC testing score of 0.886 for the Toxicity_drug dataset. Furthermore, it was observed that adding marketed drugs as negative samples not only improves the predictive performance of the binary classification Common-Toxicity task but also enhances the stability of the model prediction. It shows that the graph-based deep learning (DL) algorithms in this study can be used as a trustworthy and effective tool to assess small molecule toxicity in the development of new drugs.

Source: Journal of Applied Toxicology - February 27, 2024 Category: Toxicology Authors: Yini Zhou, Chao Ning, Yijun Tan, Yaqi Li, Jiaxu Wang, Yuanyuan Shu, Songping Liang, Zhonghua Liu, Ying Wang Tags: RESEARCH ARTICLE Source Type: research