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PMODiff: Physics-Informed Multi-Objective Optimization Diffusion Model for Protein-Specific 3D Molecule Generation.

Yaoxiang Zhang1, Shuang Wang1, Junteng Ma1

  • 1Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software, Qingdao 266580, China.

Journal of Chemical Information and Modeling
|May 21, 2025
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Summary
This summary is machine-generated.

PMODiff, a novel physics-informed diffusion model, enhances drug design by optimizing ligand-protein interactions. It generates realistic 3D structures with improved binding affinity and drug-like properties.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Artificial intelligence in medicine

Background:

  • 3D generative models are crucial for structure-based drug design.
  • Existing models often neglect physicochemical principles and drug-like properties.
  • This limits their effectiveness in practical drug development.

Purpose of the Study:

  • To develop a novel 3D generative model for drug design.
  • To integrate physics-informed principles and multi-objective optimization.
  • To improve ligand generation for enhanced binding affinity, drug-likeness, and synthetic accessibility.

Main Methods:

  • Introduced PMODiff (Physics-Informed Multi-Objective Optimization Diffusion Model).
  • Integrated a physics-informed component using a simplified Lennard-Jones potential during denoising.
  • Utilized pretrained networks for multi-objective optimization of affinity, drug-likeness, and synthesizability.

Main Results:

  • PMODiff generated more realistic 3D ligand structures.
  • Achieved higher binding affinity with an average Vina Score of -7.44.
  • Demonstrated a 13% performance improvement over existing methods on the CrossDocked2020 dataset.

Conclusions:

  • PMODiff effectively addresses limitations of current generative models in drug design.
  • The physics-informed approach enhances the generation of drug candidates with favorable properties.
  • PMODiff shows significant potential for advancing comprehensive and practical drug discovery.