A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization
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arXiv
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| Hauptverfasser: | , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866918014721458176 |
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| author | Qiao, Anjie Zhang, Hao Yuan, Qianmu Deng, Qirui Su, Jingtian Huang, Weifeng Zhou, Huihao Li, Guo-Bo Wang, Zhen Lei, Jinping |
| author_facet | Qiao, Anjie Zhang, Hao Yuan, Qianmu Deng, Qirui Su, Jingtian Huang, Weifeng Zhou, Huihao Li, Guo-Bo Wang, Zhen Lei, Jinping |
| contents | Generating molecules that bind to specific protein targets via diffusion models has shown good promise for structure-based drug design and molecule optimization. Especially, the diffusion models with binding interaction guidance enables molecule generation with high affinity through forming favorable interaction within protein pocket. However, the generated molecules may not form interactions with the highly conserved residues, which are important for protein functions and bioactivities of the ligands. Herein, we developed a new 3D target-aware diffusion model DiffDecip, which explicitly incorporates the protein-ligand binding interactions and evolutionary conservation information of protein residues into both diffusion and sampling process, for molecule optimization through scaffold decoration. The model performance revealed that DiffDecip outperforms baseline model DiffDec on molecule optimization towards higher affinity through forming more non-covalent interactions with highly conserved residues in the protein pocket. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_05874 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization Qiao, Anjie Zhang, Hao Yuan, Qianmu Deng, Qirui Su, Jingtian Huang, Weifeng Zhou, Huihao Li, Guo-Bo Wang, Zhen Lei, Jinping Machine Learning Chemical Physics Biomolecules Generating molecules that bind to specific protein targets via diffusion models has shown good promise for structure-based drug design and molecule optimization. Especially, the diffusion models with binding interaction guidance enables molecule generation with high affinity through forming favorable interaction within protein pocket. However, the generated molecules may not form interactions with the highly conserved residues, which are important for protein functions and bioactivities of the ligands. Herein, we developed a new 3D target-aware diffusion model DiffDecip, which explicitly incorporates the protein-ligand binding interactions and evolutionary conservation information of protein residues into both diffusion and sampling process, for molecule optimization through scaffold decoration. The model performance revealed that DiffDecip outperforms baseline model DiffDec on molecule optimization towards higher affinity through forming more non-covalent interactions with highly conserved residues in the protein pocket. |
| title | A 3D pocket-aware and evolutionary conserved interaction guided diffusion model for molecular optimization |
| topic | Machine Learning Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2505.05874 |