3D Molecule Generation from Rigid Motifs via SE(3) Flows

Generative AI & LLMs

Published: arXiv: 2601.16955v1

Authors

Roman Poletukhin Marcel Kollovieh Eike Eberhard Stephan Günnemann

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.

Paper Summary

Problem

The main problem addressed by this research paper is the limitation of current 3D molecular structure generation methods, which operate at the level of individual atoms and discard the rich chemical modularity inherent to molecular structures. This makes it challenging to generate molecules with complex topologies and diverse chemical motifs.

Key Innovation

The key innovation of this work is the proposal of MOTIFLOW, a novel generative framework for 3D molecules that operates on rigid motifs rather than individual atoms. MOTIFLOW decomposes molecules into chemically meaningful rigid fragments and jointly learns the discrete distribution of motif types and their continuous spatial configuration. This formulation enables the generation of high-fidelity molecular structures with significantly fewer sampling steps.

Practical Impact

This research has the potential to accelerate in-silico discovery and the design of novel molecules. By generating molecules with complex topologies and diverse chemical motifs, MOTIFLOW can help researchers and drug developers identify new lead compounds and optimize existing ones. This can lead to the development of new medicines and materials with improved properties.

Analogy / Intuitive Explanation

Imagine building a house using LEGO bricks. Each brick represents a rigid motif, and the house represents the 3D molecular structure. MOTIFLOW is like a LEGO builder that can create a house (molecule) by combining different bricks (motifs) in a specific way. The builder knows how to arrange the bricks to create a stable and functional house, just like MOTIFLOW generates stable and functional molecular structures by arranging rigid motifs.

Paper Information

Categories:

cs.LG

Published Date:

arXiv ID:

2601.16955v1

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