Classification Pretraining Enhances Performance of Target-Based Affinity Prediction for Reproductive Toxicity Assessment

Reproductive toxicity is challenging to assess because of its diverse phenotypes and complex mechanisms; target-prediction models enable early identification of toxic potential. However, affinity prediction models suffer from limited training data and poor generalization to novel chemical scaffolds, which hinders their application in prospective toxicity assessment. Here, we propose a classification pretraining-regression fine-tuning framework that leverages large-scale binary activity data to learn generalizable compound-protein interaction patterns. Systematic evaluations across six dual-encoder models and three data-split strategies demonstrate consistent performance gains. Under the most stringent cluster-split scenario, our framework achieved an average R 2 improvement of 0.324, demonstrating substantially enhanced generalization to novel chemotypes. Using the best-performing model ( R 2 = 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.