3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
<|reference_start|>3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation: 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.<|reference_end|>
@article{zhu20243m-diffusion:,
    title={3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular
  Structure Generation},
    author={Huaisheng Zhu, Teng Xiao, Vasant G Honavar},
    journal={arXiv preprint arXiv:2403.07179},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07179},
    primaryClass={cs.LG cs.CL q-bio.BM}
}