3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
This work addresses a critical task in drug discovery and materials design by enabling language-guided molecular generation, representing an incremental improvement through a novel multi-modal approach.
The authors tackled the problem of generating molecular structures with desired properties by proposing 3M-Diffusion, a multi-modal diffusion method that aligns graph latent spaces with text descriptions, resulting in high-quality, novel, and diverse molecular graphs that semantically match the text.
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.