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

Yu Ma1, Yingqing Shou1, Xing Chen1

  • 1Shanghai Key Laboratory of Air Quality and Environmental Health, National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary, Department of Environmental Science & Engineering, Fudan University, Shanghai 200433, China.

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This study introduces a new computational framework to predict chemical toxicity by learning compound-protein interactions. The method improves generalization to new chemicals, aiding in early reproductive toxicity assessment.

Keywords:
classification pretrainingcompound-target interactionreproductive toxicitystructural alerts

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Area of Science:

  • Computational toxicology
  • Drug discovery and development
  • Chemical biology

Background:

  • Assessing reproductive toxicity is complex due to diverse mechanisms and phenotypes.
  • Existing affinity prediction models struggle with limited data and poor generalization to novel chemical structures.
  • Early identification of toxic potential is crucial for prospective toxicity assessment.

Purpose of the Study:

  • To develop a robust computational framework for predicting chemical binding affinities to reproductive targets.
  • To enhance the generalization capabilities of predictive models for novel chemical scaffolds.
  • To enable interpretable, mechanism-guided reproductive toxicity assessment.

Main Methods:

  • A classification pretraining-regression fine-tuning framework was proposed.
  • Leveraged large-scale binary activity data for learning compound-protein interaction patterns.
  • Evaluated performance across six dual-encoder models and three data-split strategies.

Main Results:

  • The proposed framework demonstrated consistent performance gains, with an average R2 improvement of 0.324 under stringent conditions.
  • Achieved enhanced generalization to novel chemotypes, outperforming existing methods.
  • The best-performing model (R2 = 0.797; MSE = 0.370) predicted compound affinities across 81 reproductive targets.

Conclusions:

  • The framework significantly enhances generalization for predicting compound-protein interactions, crucial for novel chemical assessment.
  • Generated target affinity spectra that distinguished toxicity effects and identified structural alerts.
  • Enabled mechanism-guided reproductive toxicity assessment and identified key targets for sex-specific reproductive diseases.