Rotation-augmented CNNs learn equivariance easily for denoising and prediction, but only large models keep generation outputs invariant to seed rotations, and their latent codes do not identify rotated molecules as the same object.
MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation
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abstract
This work introduces MiDi, a novel diffusion model for jointly generating molecular graphs and their corresponding 3D arrangement of atoms. Unlike existing methods that rely on predefined rules to determine molecular bonds based on the 3D conformation, MiDi offers an end-to-end differentiable approach that streamlines the molecule generation process. Our experimental results demonstrate the effectiveness of this approach. On the challenging GEOM-DRUGS dataset, MiDi generates 92% of stable molecules, against 6% for the previous EDM model that uses interatomic distances for bond prediction, and 40% using EDM followed by an algorithm that directly optimize bond orders for validity. Our code is available at github.com/cvignac/MiDi.
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cs.LG 1years
2025 1verdicts
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Do we need equivariant models for molecule generation?
Rotation-augmented CNNs learn equivariance easily for denoising and prediction, but only large models keep generation outputs invariant to seed rotations, and their latent codes do not identify rotated molecules as the same object.