{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/3m-diffusion-latent-multi-modal-diffusion-for","title":"3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation","arxiv_id":"2403.07179","date":"2024-03-11","proceeding":null,"authors":["Huaisheng Zhu","Teng Xiao","Vasant G Honavar"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2403.07179v2","url_pdf":"https://arxiv.org/pdf/2403.07179v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"3m-diffusion-latent-multi-modal-diffusion-for","repo_url":"https://github.com/huaishengzhu/3mdiffusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"},{"task_slug":"graph-generation","task_name":"Graph Generation"},{"task_slug":"molecular-graph-generation","task_name":"Molecular Graph Generation"},{"task_slug":"text-guided-generation","task_name":"text-guided-generation"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"},{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}