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

About

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.

Clement Vignac, Nagham Osman, Laura Toni, Pascal Frossard• 2023

Related benchmarks

TaskDatasetResultRank
3D Molecule GenerationQM9 (test)
Validity97.9
64
3D Molecule GenerationQM9 unconditional generation
Atom Stability97.9
33
3D Molecule GenerationGEOM-DRUG (test)
Atom Stability (%)99.8
29
3D Molecule GenerationGEOM Drugs
Atom. Stability99.8
27
Unconditional Molecule GenerationQM9 (test)--
22
Unconditional 3D molecular graph generationDrugs
Atom Stability (AS)82.5
15
De novo 3D molecule generationQM9
FCD3D1.1
12
Molecule GenerationQM9 (test)
Atom Stability99.8
9
De novo 3D molecule generationGEOM Drugs
FCD3D23.14
9
Unconditional Molecule GenerationQM9 explicit hydrogens (test)
Molecular Stability97.5
6
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