SemlaFlow -- Efficient 3D Molecular Generation with Latent Attention and Equivariant Flow Matching
This addresses efficiency and quality issues in structure-based drug design, though it appears incremental as it builds on existing 3D generation methods.
The authors tackled the problem of slow sampling and poor chemical validity in 3D molecular generation by proposing SemlaFlow, an E(3)-equivariant flow matching model that achieves state-of-the-art results with a two order-of-magnitude speedup using only 20 sampling steps.
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.