Pocket2Mol

Equivariant diffusion model for structure-based drug design that generates novel 3D molecules directly inside protein binding pockets. Published in Nature Computational Science 2024.

Pocket-based 3D molecule generation combining language model token prediction with geometric deep learning for 3D coordinate generation. Published in Nature Machine Intelligence 2024.

Flow-based generative model that creates novel small molecule ligands for a target binding pocket. Generates hundreds of candidates in minutes.

Diffusion-based generative model for de novo protein backbone design. Generates novel protein structures conditioned on binding targets, symmetry, or functional sites.

Successor to RFdiffusion using flow matching. Designs enzymes directly from active site geometry (theozyme) specifications.

Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.