A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network.

ABSTRACT: Reproductive toxicity is a concern critical to human health and chemical safety assessment. Recently, the U.S. Food and Drug Administration announced plans to assess toxicity with artificial intelligence-based computational models instead of animal studies in "a win-win for public health and ethics." In this study, we used a reproductive toxicity dataset using Simplified Molecular Input Line Entry Specifications (SMILES) to represent 1091 reproductively toxic and 1063 non-toxic small-molecule compounds. A repeated nested cross-validation procedure was applied, in which the dataset was randomly partitioned into five distinct folds in the outer loop, each time, one fold serving as the test set. In the inner loop, a similar procedure was also repeated five times, with 12.5% each time serving

SUBMITTER: He O 

PROVIDER: S-EPMC12321834 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature