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

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

17 Feb 2023arXiv:2302.09048archive 2025-07-28

Clement Vignac, Nagham Osman, Laura Toni, Pascal Frossard

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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Code

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cvignac/midi officialmentioned in papermentioned on GitHubpytorchMIT report

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1ran · our draft was wrong
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effective_batch_size cvignac/midi/midi/datasets/adaptive_loader.py official repository ran MIT (permissive) · 6dcb1a7a685a56a3 · report
load_pickle cvignac/midi/midi/analysis/analyse_charges.py official repository ran · our draft was wrong MIT (permissive) · 70db7c9cbe9e680d · report
build_xae_molecule cvignac/midi/midi/analysis/baselines_evaluation.py official repository unverified MIT (permissive) · 537165607cf63f8e · report
check_stability cvignac/midi/midi/analysis/rdkit_functions.py official repository unverified MIT (permissive) · 2f2b4dc88b682898 · report
geom_predictor cvignac/midi/midi/analysis/baselines_evaluation.py official repository unverified MIT (permissive) · ab8617fd28b92b3c · report
get_bond_order cvignac/midi/midi/analysis/baselines_evaluation.py official repository unverified MIT (permissive) · 8472ecb13cf2144c · report
masked_softmax cvignac/midi/midi/models/layers.py official repository unverified MIT (permissive) · 35df91e0cf6e7565 · report
maybe_subset cvignac/midi/midi/datasets/abstract_dataset.py official repository unverified MIT (permissive) · 12573f6a644aba8f · report
plot_molecule3d cvignac/midi/midi/analysis/visualization.py official repository unverified MIT (permissive) · 3e1c2c62a1bf6189 · report
remove_hydrogens cvignac/midi/midi/datasets/dataset_utils.py official repository unverified MIT (permissive) · 43b3ed740aac1a3c · report
remove_mean_with_mask cvignac/midi/midi/utils.py official repository unverified MIT (permissive) · 250da8c7f72b64ed · report
setup_wandb cvignac/midi/midi/utils.py official repository unverified MIT (permissive) · be7738474afcfd15 · report
to_dense cvignac/midi/midi/utils.py official repository unverified MIT (permissive) · c431df61de63102e · report

Tasks

DenoisingUnconditional Molecule Generation

Datasets

Introduced by this paper, per the archive.

GEOM-DRUGS

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Unconditional Molecule Generation GEOM-DRUGS MiDi Validity 77.8 #5 of 5 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Diffusion

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