Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866917615641821184 |
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| author | Lin, Haitao Huang, Yufei Zhang, Odin Wu, Lirong Li, Siyuan Chen, Zhiyuan Li, Stan Z. |
| author_facet | Lin, Haitao Huang, Yufei Zhang, Odin Wu, Lirong Li, Siyuan Chen, Zhiyuan Li, Stan Z. |
| contents | In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To solve this, we propose D3FG, a functional-group-based diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. And the two kinds of components can together form complicated fragments that enhance ligand-protein interactions.
To be specific, in the diffusion process, D3FG diffuses the data distribution of the positions, orientations, and types of the components into a prior distribution; In the generative process, the noise is gradually removed from the three variables by denoisers parameterized with designed equivariant graph neural networks. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. Besides, D3FG as a solution to a new task of molecule elaboration, could generate molecules with high affinities based on existing ligands and the hotspots of target proteins. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_13769 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration Lin, Haitao Huang, Yufei Zhang, Odin Wu, Lirong Li, Siyuan Chen, Zhiyuan Li, Stan Z. Biomolecules Machine Learning In recent years, AI-assisted drug design methods have been proposed to generate molecules given the pockets' structures of target proteins. Most of them are atom-level-based methods, which consider atoms as basic components and generate atom positions and types. In this way, however, it is hard to generate realistic fragments with complicated structures. To solve this, we propose D3FG, a functional-group-based diffusion model for pocket-specific molecule generation and elaboration. D3FG decomposes molecules into two categories of components: functional groups defined as rigid bodies and linkers as mass points. And the two kinds of components can together form complicated fragments that enhance ligand-protein interactions. To be specific, in the diffusion process, D3FG diffuses the data distribution of the positions, orientations, and types of the components into a prior distribution; In the generative process, the noise is gradually removed from the three variables by denoisers parameterized with designed equivariant graph neural networks. In the experiments, our method can generate molecules with more realistic 3D structures, competitive affinities toward the protein targets, and better drug properties. Besides, D3FG as a solution to a new task of molecule elaboration, could generate molecules with high affinities based on existing ligands and the hotspots of target proteins. |
| title | Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2306.13769 |