A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network
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....
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Toxicology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/ftox.2025.1640612/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387663570763776 |
|---|---|
| author | Owen He Daoxing Chen Yimei Li |
| author_facet | Owen He Daoxing Chen Yimei Li |
| author_sort | Owen He |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-74cc7fdf2b15471ca1137f1ff8a8bed7 |
| institution | Kabale University |
| issn | 2673-3080 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Toxicology |
| spelling | doaj-art-74cc7fdf2b15471ca1137f1ff8a8bed72025-08-20T03:51:29ZengFrontiers Media S.A.Frontiers in Toxicology2673-30802025-07-01710.3389/ftox.2025.16406121640612A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural networkOwen He0Daoxing Chen1Yimei Li2Deerfield Academy, Deerfield, MA, United StatesSchool of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, ChinaDepartment of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, United StatesReproductive 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.https://www.frontiersin.org/articles/10.3389/ftox.2025.1640612/fullreproductiveartificial intelligence (AI)deep learninggraph neural networkCMPNNin silico |
| spellingShingle | Owen He Daoxing Chen Yimei Li A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network Frontiers in Toxicology reproductive artificial intelligence (AI) deep learning graph neural network CMPNN in silico |
| title | A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| title_full | A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| title_fullStr | A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| title_full_unstemmed | A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| title_short | A deep-learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| title_sort | deep learning approach to predict reproductive toxicity of chemicals using communicative message passing neural network |
| topic | reproductive artificial intelligence (AI) deep learning graph neural network CMPNN in silico |
| url | https://www.frontiersin.org/articles/10.3389/ftox.2025.1640612/full |
| work_keys_str_mv | AT owenhe adeeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork AT daoxingchen adeeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork AT yimeili adeeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork AT owenhe deeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork AT daoxingchen deeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork AT yimeili deeplearningapproachtopredictreproductivetoxicityofchemicalsusingcommunicativemessagepassingneuralnetwork |