Structure-aware generation of drug-like molecules
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Conference Name
Machine Learning in Structural Biology Workshop at the 35th Conference on Neural Information Processing Systems
Type
Article
This Version
AM
Metadata
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Drotár, P., Jamasb, A., Day, B., Cangea, C., & Lio, P. Structure-aware generation of drug-like molecules. Machine Learning in Structural Biology Workshop at the 35th Conference on Neural Information Processing Systems. https://doi.org/10.17863/CAM.79076
Abstract
Structure-based drug design involves finding ligand molecules that exhibit
structural and chemical complementarity to protein pockets. Deep generative
methods have shown promise in proposing novel molecules from scratch (de-novo
design), avoiding exhaustive virtual screening of chemical space. Most
generative de-novo models fail to incorporate detailed ligand-protein
interactions and 3D pocket structures. We propose a novel supervised model that
generates molecular graphs jointly with 3D pose in a discretised molecular
space. Molecules are built atom-by-atom inside pockets, guided by structural
information from crystallographic data. We evaluate our model using a docking
benchmark and find that guided generation improves predicted binding affinities
by 8% and drug-likeness scores by 10% over the baseline. Furthermore, our model
proposes molecules with binding scores exceeding some known ligands, which
could be useful in future wet-lab studies.
Keywords
q-bio.QM, q-bio.QM, cs.LG
Embargo Lift Date
2022-12-13
Identifiers
This record's DOI: https://doi.org/10.17863/CAM.79076
This record's URL: https://www.repository.cam.ac.uk/handle/1810/331624
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