Structure-based Drug Design with Equivariant Diffusion Models
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 a realistic binding scenario that
complements 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.
Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, Michael Bronstein, Bruno Correia