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DTF-diffusion: A 3D equivariant diffusion generation model based on ligand-target information fusion.

Jianxin Wang1, Yongxin Zhu1, Yushuang Liu2

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China.

Computational Biology and Chemistry
|February 28, 2025
PubMed
Summary

This study introduces DTF-diffusion, a novel deep learning model for drug discovery that fuses 3D ligand and target information. It generates more chemically valid drug molecules by incorporating interaction data and chemical rules.

Keywords:
Chemical rule constraintDeep learningDiffusion modelDrug molecular generationDrug-target information fusion

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Area of Science:

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

Background:

  • Deep learning models for drug discovery aim to generate molecules that bind to target proteins.
  • Three-dimensional (3D) molecular structures offer superior performance over 2D models in drug discovery.
  • Current 3D deep generative models often neglect crucial ligand-target interaction information and chemical knowledge, leading to unrealistic molecular structures.

Purpose of the Study:

  • To propose DTF-diffusion, a novel 3D equivariant diffusion model for drug molecule generation.
  • To address limitations in existing models by integrating ligand-target interaction information and chemical rules.
  • To enhance the rationality and validity of generated drug molecules in silico.

Main Methods:

  • DTF-diffusion utilizes a diffusion model framework.
  • A multimodal feature fusion module integrates 3D positional features of ligands and targets, extracting advanced hidden features from ligand atom and target sequence information.
  • A chemical rule discrimination module is employed to learn and enforce chemical rules in generated molecular structures.

Main Results:

  • DTF-diffusion demonstrated superior performance compared to baseline methods on the CrossDock2020 dataset.
  • The model achieved a 3.85% increase in the drug-likeness index and a 4.34% increase in the drug validity index compared to the previous optimal model.
  • Extensive generation experiments confirmed the excellent performance and potential of DTF-diffusion.

Conclusions:

  • DTF-diffusion effectively fuses ligand-target interaction information and chemical knowledge for improved 3D molecular generation.
  • The proposed model significantly enhances the drug-likeness and validity of generated molecules.
  • DTF-diffusion shows promising application prospects in accelerating the drug discovery process.