Summary of 3m-diffusion: Latent Multi-modal Diffusion For Language-guided Molecular Structure Generation, by Huaisheng Zhu et al.
3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
by Huaisheng Zhu, Teng Xiao, Vasant G Honavar
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-modal molecular graph generation method, called 3M-Diffusion, is proposed to generate diverse, ideally novel molecular structures with desired properties. This approach encodes molecular graphs into a graph latent space aligned with a text space learned from textual descriptions. A probabilistic mapping is then learned from the text space to the latent molecular graph space using a diffusion model. The method demonstrates high-quality, novel, and diverse molecular graph generation that semantically matches textual descriptions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generating molecules with specific properties is crucial for drug discovery and materials design. A new way to create these molecules, called 3M-Diffusion, uses both text and molecule information. It takes in a description of the molecule you want and generates it from scratch. The results show that this method can make many different, high-quality molecules that match what you described. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Latent space * Multi modal
