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Structure-based Drug Design and Molecular Optimisation with Diffusion Models

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If you have a question about this talk, please contact Mateja Jamnik .

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.

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This talk is part of the Artificial Intelligence Research Group Talks (Computer Laboratory) series.

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Seminar Room 2. Institute for Manufacturing, West Cambridge FW26, William Gates Building. Zoom link: https://cl-cam-ac-uk.zoom.us/j/4361570789?pwd=Nkl2T3ZLaTZwRm05bzRTOUUxY3Q4QT09&from=addon Junior Mirror Symmetry Seminars

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