bibtext: @inproceedings{
  zhu2024mdiffusion,
  title={3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation},
  author={Huaisheng Zhu and Teng Xiao and Vasant G Honavar},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=DomBynQsqt}
  }
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.
title: 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation
authors: Huaisheng Zhu Teng Xiao Vasant G Honavar