3D Molecule Generation from Rigid Motifs via SE(3) Flows
🤖 AI Summary
Traditional 3D molecular generation methods, which treat atoms as fundamental units, struggle to efficiently model complex molecular structures. This work proposes a novel paradigm by representing molecules as collections of rigid structural motifs and, for the first time, integrates SE(3)-equivariant generative flows for 3D molecular generation. The proposed approach substantially improves both generation efficiency and representation compactness. On the GEOM-Drugs dataset, it achieves superior atomic stability compared to existing methods while reducing the number of generation steps by a factor of 2–10 and attaining a 3.5× compression ratio in molecular representation.
Application Category
📝 Abstract
Three-dimensional molecular structure generation is typically performed at the level of individual atoms, yet molecular graph generation techniques often consider fragments as their structural units. Building on the advances in frame-based protein structure generation, we extend these fragmentation ideas to 3D, treating general molecules as sets of rigid-body motifs. Utilising this representation, we employ SE(3)-equivariant generative modelling for de novo 3D molecule generation from rigid motifs. In our evaluations, we observe comparable or superior results to state-of-the-art across benchmarks, surpassing it in atom stability on GEOM-Drugs, while yielding a 2x to 10x reduction in generation steps and offering 3.5x compression in molecular representations compared to the standard atom-based methods.
Problem
Research questions and friction points this paper is trying to address.
3D molecule generation
rigid motifs
SE(3) flows
molecular representation
de novo molecular design
Innovation
Methods, ideas, or system contributions that make the work stand out.
SE(3)-equivariant generative modeling
rigid motifs
3D molecule generation
fragment-based representation
molecular compression
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