3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929525210742784 |
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| author | Zhu, Huaisheng Xiao, Teng Honavar, Vasant G |
| author_facet | Zhu, Huaisheng Xiao, Teng Honavar, Vasant G |
| contents | Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally novel molecular structures with desired properties. 3M-Diffusion encodes molecular graphs into a graph latent space which it then aligns with the text space learned by encoder-based LLMs from textual descriptions. It then reconstructs the molecular structure and atomic attributes based on the given text descriptions using the molecule decoder. It then learns a probabilistic mapping from the text space to the latent molecular graph space using a diffusion model. The results of our extensive experiments on several datasets demonstrate that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07179 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation Zhu, Huaisheng Xiao, Teng Honavar, Vasant G Machine Learning Computation and Language Biomolecules Generating molecular structures with desired properties is a critical task with broad applications in drug discovery and materials design. We propose 3M-Diffusion, a novel multi-modal molecular graph generation method, to generate diverse, ideally novel molecular structures with desired properties. 3M-Diffusion encodes molecular graphs into a graph latent space which it then aligns with the text space learned by encoder-based LLMs from textual descriptions. It then reconstructs the molecular structure and atomic attributes based on the given text descriptions using the molecule decoder. It then learns a probabilistic mapping from the text space to the latent molecular graph space using a diffusion model. The results of our extensive experiments on several datasets demonstrate that 3M-Diffusion can generate high-quality, novel and diverse molecular graphs that semantically match the textual description provided. |
| title | 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation |
| topic | Machine Learning Computation and Language Biomolecules |
| url | https://arxiv.org/abs/2403.07179 |