Geometric Representation Condition Improves Equivariant Molecule Generation

• URL: http://arxiv.org/abs/2410.03655v2
• Date: Mon, 10 Feb 2025 06:34:22 GMT
• Title: Geometric Representation Condition Improves Equivariant Molecule Generation
• Authors: Zian Li, Cai Zhou, Xiyuan Wang, Xingang Peng, Muhan Zhang,
• Abstract summary: We introduce GeoRCG, a framework to enhance the performance of molecular generative models. We decompose the molecule generation process into two stages: first, generating an informative geometric representation; second, generating a molecule conditioned on the representation. We observe significant quality improvements in unconditional molecule generation tasks on the widely-used QM9 and GEOM-DRUG datasets.
• Score: 24.404588237915732
• License:
• Abstract: Recent advancements in molecular generative models have demonstrated substantial potential in accelerating scientific discovery, particularly in drug design. However, these models often face challenges in generating high-quality molecules, especially in conditional scenarios where specific molecular properties must be satisfied. In this work, we introduce GeoRCG, a general framework to enhance the performance of molecular generative models by integrating geometric representation conditions with provable theoretical guarantees. We decompose the molecule generation process into two stages: first, generating an informative geometric representation; second, generating a molecule conditioned on the representation. Compared to directly generating a molecule, the relatively easy-to-generate representation in the first stage guides the second-stage generation to reach a high-quality molecule in a more goal-oriented and much faster way. Leveraging EDM and SemlaFlow as the base generators, we observe significant quality improvements in unconditional molecule generation tasks on the widely-used QM9 and GEOM-DRUG datasets. More notably, in the challenging conditional molecular generation task, our framework achieves an average 31\% performance improvement over state-of-the-art approaches, highlighting the superiority of conditioning on semantically rich geometric representations over conditioning on individual property values as in previous approaches. Furthermore, we show that, with such representation guidance, the number of diffusion steps can be reduced to as small as 100 while largely preserving the generation quality achieved with 1,000 steps, thereby significantly accelerating the generation process.

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