Talk: Structure-based Drug Design and Molecular Optimisation with Diffusion Models
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 talk, I will reframe SBDD as a 3D-conditional generation problem and present DiffSBDD, an SE(3)-equivariant 3D-conditional diffusion model that generates novel ligands conditioned on protein pockets. 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. Furthermore, we show that DiffSBDD is capable of molecular optimisation and redesign out-of-the-box using inpainting.
Charlie is a PhD Student at the University of Cambridge where he is supervised by Prof Sir Tom Blundell and Prof Pietro Lio. He has a background in Biochemsitry and a passion for AI technology to solve problems in structural biology and drug discovery, in particular with diffusion modelling and Geomtric Deep Learning.