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Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking

Hannes H Loeffler1, Shunzhou Wan2, Marco Klähn1

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Summary

This study introduces a generative active learning (GAL) protocol combining AI and physics simulations to discover novel drug ligands. The GAL protocol efficiently identifies high-scoring, diverse molecules for drug discovery targets.

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Area of Science:

  • Computational chemistry and cheminformatics
  • Artificial intelligence in drug discovery
  • Molecular modeling and simulation

Background:

  • Active learning (AL) accelerates molecular discovery by intelligently selecting data points, mimicking the iterative design-make-test-analysis cycle.
  • Traditional drug discovery is often lengthy and resource-intensive, necessitating more efficient computational approaches.
  • Generative AI and physics-based simulations offer complementary strengths for exploring vast chemical spaces.

Purpose of the Study:

  • To develop and deploy a generative active learning (GAL) protocol for discovering novel molecular ligands.
  • To combine generative molecular AI (REINVENT) with physics-based simulations (ESMACS) for enhanced ligand discovery.
  • To assess the efficiency and effectiveness of the GAL protocol on large-scale computing infrastructure (Frontier).

Main Methods:

  • Implementation of a GAL protocol integrating REINVENT for molecular generation and ESMACS for binding free energy calculations.
  • Application of the GAL protocol to identify ligands for two target proteins: 3CLpro and TNKS2.
  • Systematic variation of batch sizes for free energy assessment to optimize protocol efficiency.

Main Results:

  • The GAL protocol successfully identified higher-scoring molecules compared to baseline methods for both 3CLpro and TNKS2 targets.
  • Discovered ligands exhibited significant chemical diversity and occupied distinct chemical spaces.
  • Analysis provided optimal batch size recommendations for different scenarios within the GAL protocol.

Conclusions:

  • The combined AI and physics-based approach offers a powerful strategy for effective chemical space sampling in drug discovery.
  • The GAL protocol demonstrates unprecedented scale and efficiency, relevant to modern data-driven drug discovery.
  • This methodology accelerates the identification of optimized compounds for specific design tasks.