3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation

• URL: http://arxiv.org/abs/2403.07179v2
• Date: Wed, 02 Oct 2024 20:09:18 GMT
• Title: 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
• Authors: Huaisheng Zhu, Teng Xiao, Vasant G Honavar,
• Abstract summary: 3M-Diffusion is a novel multi-modal molecular graph generation method. It generates diverse, ideally novel molecular structures with desired properties.
• Score: 18.55127917150268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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