Structure-based Drug Design with Equivariant Diffusion Models

Arne Schneuing, Yuanqi Du, Charles Harris, Arian Rokkum Jamasb, Ilia Igashov, weitao Du, Tom Leon Blundell, Pietro Lio, Carla P Gomes, Max Welling, Michael M. Bronstein, Bruno Correia

Keywords: Diffusion Models, Equivariant Neural Networks, Structure-based Drug Design, Molecule Generation, Conditional Generation

Abstract:

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with high affinity and specificity to pre-determined protein targets. Traditional SBDD pipelines start with large-scale docking of compound libraries from public databases, thus limiting the exploration of chemical space to existent previously studied regions.
Recent machine learning methods approached this problem using an atom-by-atom generation approach, which is computationally expensive.
In this paper, we formulate SBDD as a 3D-conditional generation problem and present DiffSBDD, an E(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets.
Furthermore, we curate a new dataset of experimentally determined binding complex data from Binding MOAD to provide realistic binding scenario rather than the synthetic CrossDocked dataset. Comprehensive in silico experiments demonstrate the efficiency of DiffSBDD in generating novel and diverse drug-like ligands that engage protein pockets with high binding energies as predicted by in silico docking.

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