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Structure-aware generation of molecules in protein pockets

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Searching the vast chemical space for potential drugs is intractable even for computational screening approaches, though deep generative methods have shown promise in proposing novel molecules from scratch (de-novo design). However, most models fail to incorporate 3D protein structures that are salient in designing drugs that bind to protein targets. I present a novel supervised approach that combines insights from graph-generative and 3D models and constructs drugs inside protein pockets, atom-by-atom, guided by structural information from crystallographic drug datasets. I find that the model improves binding affinities on average by 8% and druglikeness scores by 10% over the non-guided baseline, and proposes some molecules that exceed the binding scores of known ligands.

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

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