He Owen, Chen Daoxing, Li Yimei
Deerfield Academy, Deerfield, MA, United States.
School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, China.
Front Toxicol. 2025 Jul 22;7:1640612. doi: 10.3389/ftox.2025.1640612. eCollection 2025.
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 as the validation set. We first evaluated the performance of classical machine learning (ML) methods such as Random Forest and Extreme Gradient Boosting on predicting reproductive toxicity, using standard model evaluation metrics including accuracy score (ACC), the area under the curve (AUC) of the receiver operating characteristics curve (ROC) and F1 score. Our analyses indicate that these methods' overall results were mediocre and insufficient for high-throughput screening. To overcome these limitations, we adopted the Communicative Message Passing Neural Network (CMPNN) framework, which incorporates a communicative kernel and a message booster module. Our results show that our ReproTox-CMPNN model outperforms the current best baselines in both embedding quality and predictive accuracy. In independent test sets, ReproTox-CMPNN achieved a mean AUC of 0.946, ACC of 0.857 and F1 score of 0.846, surpassing traditional algorithms to establish itself as a new state-of-the-art model in this field. These findings demonstrate that CMPNN's deep capture of multi-level molecular relationships offers an efficient and reliable computational tool for rapid chemical safety screening and risk assessment.
生殖毒性是人类健康和化学安全评估的关键问题。最近，美国食品药品监督管理局宣布计划使用基于人工智能的计算模型来评估毒性，而不是进行动物研究，称这是“对公共卫生和伦理的双赢”。在本研究中，我们使用了一个生殖毒性数据集，该数据集使用简化分子输入线性规范（SMILES）来表示1091种具有生殖毒性的小分子化合物和1063种无毒小分子化合物。应用了重复嵌套交叉验证程序，其中数据集在外循环中被随机划分为五个不同的折，每次有一个折作为测试集。在内循环中，也重复类似的程序五次，每次有12.5%作为验证集。我们首先使用包括准确率得分（ACC）、接收者操作特征曲线（ROC）的曲线下面积（AUC）和F1得分等标准模型评估指标，评估了随机森林和极端梯度提升等经典机器学习（ML）方法在预测生殖毒性方面的性能。我们的分析表明，这些方法的总体结果一般，不足以进行高通量筛选。为了克服这些局限性，我们采用了通信消息传递神经网络（CMPNN）框架，该框架包含一个通信内核和一个消息增强模块。我们的结果表明，我们的ReproTox - CMPNN模型在嵌入质量和预测准确性方面均优于当前最佳基线。在独立测试集中，ReproTox - CMPNN的平均AUC为0.946，ACC为0.857，F1得分为0.846，超过了传统算法，成为该领域新的最先进模型。这些发现表明，CMPNN对多级分子关系的深度捕捉为快速化学安全筛选和风险评估提供了一种高效且可靠的计算工具。
