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Related Experiment Videos

Improving hit discovery by integrating activity cliff sensitivity into active learning.

Junha Kim1, Youngkuk Kim1,2, Bonil Koo3

  • 1Department of Computer Science and Engineering, Seoul National University, Seoul, 08826, Republic of Korea.

Bioinformatics (Oxford, England)
|July 7, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an activity cliff-aware active learning framework to improve molecular discovery. The novel approach enhances hit discovery efficiency by prioritizing informative molecules for labeling, outperforming existing methods in data-limited scenarios.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Active learning accelerates molecular discovery but often overlooks activity cliffs, which are sharp bioactivity changes from minor structural modifications.
  • Existing methods may lead to suboptimal sample selection by focusing on global molecular information.

Purpose of the Study:

  • To propose a model-agnostic, activity cliff-aware active learning framework to enhance hit discovery efficiency.
  • To improve sample selection in active learning by explicitly considering local structure-activity relationships.

Main Methods:

  • Developed an auxiliary activity cliff scoring module trained on pairwise molecular relationships to capture local structure-activity sensitivity.
  • Integrated cliff scores into an acquisition function to prioritize informative molecules for labeling.
  • Ensured the framework is model-agnostic, compatible with various molecular representations and active learning pipelines.

Main Results:

  • The proposed method consistently identified more active compounds than baseline strategies across benchmark datasets.
  • Demonstrated improved robustness in early-stage, data-scarce learning scenarios.
  • Ablation studies confirmed the significant contribution of activity cliff awareness to performance gains.

Conclusions:

  • Explicitly modeling activity cliffs is crucial for effective active learning in drug discovery.
  • The model-agnostic design facilitates integration and accelerates hit discovery in data-limited settings.
  • The framework offers a robust strategy for efficient molecular discovery.