Papers › SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant...
SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson
Methods for jointly generating molecular graphs along with their 3D conformations have gained prominence recently due to their potential impact on structure-based drug design. Current approaches, however, often suffer from very slow sampling times or generate molecules with poor chemical validity. Addressing these limitations, we propose Semla, a scalable E(3)-equivariant message passing architecture. We further introduce an unconditional 3D molecular generation model, SemlaFlow, which is trained using equivariant flow matching to generate a joint distribution over atom types, coordinates, bond types and formal charges. Our model produces state-of-the-art results on benchmark datasets with as few as 20 sampling steps, corresponding to a two order-of-magnitude speedup compared to state-of-the-art. Furthermore, we highlight limitations of current evaluation methods for 3D generation and propose new benchmark metrics for unconditional molecular generators. Finally, using these new metrics, we compare our model's ability to generate high quality samples against current approaches and further demonstrate SemlaFlow's strong performance.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Unconditional Molecule Generation | GEOM-DRUGS | SemlaFlow | PoseBusters Atoms Connected | 92.3 | #2 of 5 | Archive leaderboard | report |
| Unconditional Molecule Generation | GEOM-DRUGS | SemlaFlow | PoseBusters Validity | 87.5 | #2 of 5 | Archive leaderboard | report |
| Unconditional Molecule Generation | GEOM-DRUGS | SemlaFlow | Validity | 93.9 | #2 of 5 | Archive leaderboard | report |
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