  Generative models for 3D drug design have gained prominence recently for
their potential to design ligands directly within protein pockets. 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 a molecular generation model, SemlaFlow, which is trained using flow
matching along with scale optimal transport, a novel extension of equivariant
optimal transport. Our model produces state-of-the-art results on benchmark
datasets with just 100 sampling steps. Crucially, SemlaFlow samples high
quality molecules with as few as 20 steps, corresponding to a two
order-of-magnitude speed-up compared to state-of-the-art, without sacrificing
performance. 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.
