Equivariant 3D-Conditional Diffusion Model for De Novo Drug Design

De novo drug design speeds up drug discovery, mitigating its time and cost burdens with advanced computational methods. Previous work either insufficiently utilized the 3D geometric structure of the target proteins, or generated ligands in an order that was inconsistent with real physics. Here we pr...

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Veröffentlicht in:IEEE journal of biomedical and health informatics Jg. 29; H. 3; S. 1805 - 1816
Hauptverfasser: Zheng, Jia, Yi, Hai-Cheng, You, Zhu-Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2025
Schlagworte:
artificial intelligence
Atoms
Bioinformatics
Chemicals
Computational Biology - methods
Diffusion models
Drug Design
Drug discovery
Drugs
equivariant diffusion model
fragment-based drug design
Graph neural networks
Humans
Ligands
Models, Molecular
molecular generation
Neural Networks, Computer
Point cloud compression
Proteins
Proteins - chemistry
Proteins - metabolism
Three-dimensional displays
ISSN:2168-2194, 2168-2208, 2168-2208
Online-Zugang:Volltext
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Zusammenfassung:De novo drug design speeds up drug discovery, mitigating its time and cost burdens with advanced computational methods. Previous work either insufficiently utilized the 3D geometric structure of the target proteins, or generated ligands in an order that was inconsistent with real physics. Here we propose an equivariant 3D-conditional diffusion model, named DiffFBDD, for generating new pharmaceutical compounds based on 3D geometric information of specific target protein pockets. DiffFBDD overcomes the underutilization of geometric information by integrating full atomic information of pockets to backbone atoms using an equivariant graph neural network. Moreover, we develop a diffusion approach to generate drugs by generating ligand fragments for specific protein pockets, which requires fewer computational resources and less generation time (65.98% <inline-formula><tex-math notation="LaTeX">\sim</tex-math></inline-formula> 96.10% lower). DiffFBDD offers better performance than state-of-the-art models in generating ligands with strong binding affinity to specific protein pockets, while maintaining high validity, uniqueness, and novelty, with clear potential for exploring the drug-like chemical space.
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ISSN:2168-2194
2168-2208
2168-2208
DOI:10.1109/JBHI.2024.3491318