Reproduce “Toward Better Geometric Representations for Molecule Generative Models” on OpenResearch

About this paper

Shaoheng Yan, Zian Li, Cai Zhou, Qiaojing Huang, Kai Liu, Muhan Zhang

LENSEs, a framework developed by researchers from PKU and MIT, enhances geometric representations for molecule generative models by dynamically optimizing the latent space during training. This approach yields improved molecule generation quality and establishes a smoother, more informative representation manifold, evidenced by higher validity and physical plausibility on molecular benchmarks.

Key insights

• The quality of geometric representations in molecule generative models is not static but can be systematically refined through explicit optimization during the generative training process.
• Reducing the Lipschitz constant and increasing the effective rank of the latent space results in a smoother, more informative manifold, improving the stability and sampling of Representation Diffusion Models.
• Leveraging hierarchical features from intermediate encoder layers and aligning them with the generator's internal states significantly improves generation quality and efficiency.

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