Classification Pretraining Enhances Performance of Target-Based Affinity Prediction for Reproductive Toxicity Assessment
= 0.797; MSE = 0.370), we predicted compound affinities across 81 reproductive targets, generating target affinity spectra that distinguished toxicity effects, identified both broad-spectrum and target-specific structural alerts, and revealed key targets for six sex-specific reproductive diseases. By predicting binding affinity, our framework not only enhances generalization but also enables interpretable, mechanism-guided reproductive toxicity assessment.
Authors
- Bin Wang (ORCID: https://orcid.org/0000-0002-1164-8430)
- Jun Xu
- Mingliang Fang (ORCID: https://orcid.org/0000-0002-2204-9783)
- Yingqing Shou (ORCID: https://orcid.org/0009-0006-5276-3749)
- Zhe Mo (ORCID: https://orcid.org/0000-0003-0375-509X)
- Yuan Chi (ORCID: https://orcid.org/0000-0002-3469-2290)
- Ailin Zhao
- Xing Chen
- Yu Ma
Institutions
- Peking University (CN)
- Fudan University (CN)
- National Health and Family Planning Commission (CN)
- Quality Research (US)
- Zhejiang Center for Disease Control and Prevention (CN)
Publication Details
- Journal
- Environmental Science & Technology
- Published
- 2026-04-15
- DOI
- https://doi.org/10.1021/acs.est.5c18296
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Chinese Academy of Sciences
- National Key Research and Development Program of China Stem Cell and Translational Research