  Generating molecules with desired properties is a critical task with broad
applications in drug discovery and materials design. Inspired by recent
advances in large language models, there is a growing interest in using natural
language descriptions of molecules to generate molecules with the desired
properties. Most existing methods focus on generating molecules that precisely
match the text description. However, practical applications call for methods
that generate diverse, and ideally novel, molecules with the desired
properties. We propose 3M-Diffusion, a novel multi-modal molecular graph
generation method, to address this challenge. 3M-Diffusion first encodes
molecular graphs into a graph latent space aligned with text 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.
