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MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation

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arxiv 2302.09048 v2 pith:ZYTZ74QL submitted 2023-02-17 cs.LG

classification cs.LG
keywords midiapproachbonddiffusiongenerationmodelmolecularmolecule
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Do we need equivariant models for molecule generation?

    cs.LG 2025-07 conditional novelty 6.0 of 10

    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 th...

  2. Semantically Consistent Discrete Diffusion for 3D Biological Graph Modeling

    cs.CV 2025-07 conditional novelty 5.0 of 10

    A stochastic label-consistent projector during discrete diffusion sampling enforces semantic edge-label constraints, yielding anatomically valid 3D vessel and airway graphs that improve downstream labeling and support...

  3. Predictive Feature Caching for Training-free Acceleration of Molecular Geometry Generation

    cs.LG 2025-10 conditional novelty 4.0 of 10

    Predictive feature caching, borrowed from image diffusion, speeds up molecular flow-matching generation by 2-3x at near-matched quality by forecasting hidden features instead of recomputing them.

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