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In-silico 3D molecular editing through physics-informed and preference-aligned generative foundation models.

Xiaohan Lin1, Yijie Xia1, Yanheng Li1

  • 1New Cornerstone Science Laboratory, Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing, China.

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|July 2, 2025
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Summary

This study introduces MolEdit, a generative AI for 3D molecules, overcoming limitations in drug design by integrating physics. MolEdit generates valid, complex molecular structures with improved stability and diversity.

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Area of Science:

  • Computational chemistry
  • Artificial intelligence in drug discovery
  • Molecular modeling

Background:

  • Generating 3D molecular structures with desired properties is crucial for drug and material design.
  • Existing generative AI (GenAI) struggles with molecular complexity, hindering applications in this field.
  • Current methods often rely heavily on domain-specific models and prior knowledge.

Purpose of the Study:

  • To bridge the gap between GenAI for images and molecules for 3D molecular generation.
  • To develop a pre-trained foundation model for molecular generation.
  • To address challenges like symmetry, stability, and entropy in molecular generation.

Main Methods:

  • Derived theoretical guidelines to pre-train foundation models for 3D molecular generation.
  • Developed a simple, model-agnostic training protocol to handle molecular complexities.
  • Applied physics-informed strategies to the MolEdit multimodal GenAI model.
  • Ensured adherence to physics laws and contextual preferences to minimize hallucinations.

Main Results:

  • MolEdit generates valid molecules with comprehensive symmetry and improved balance between stability and diversity.
  • The model successfully handles complex 3D scaffolds that challenge other methods.
  • MolEdit demonstrates zero-shot capability for lead optimization and linker design based on specifications.

Conclusions:

  • MolEdit serves as a foundation model for AI-aided molecular editing and manipulation.
  • The approach offers flexibility and developability for various applications in computer-aided design.
  • Physics-informed strategies enhance the reliability and applicability of GenAI in molecular design.