Structure-aware generation of drug-like molecules
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Authors
Drotár, Pavol
Jamasb, Arian Rokkum
Day, Ben
Cangea, Cătălina
Liò, Pietro
Publication Date
2021-12-13Conference Name
Machine Learning in Structural Biology Workshop at the 35th Conference on Neural Information Processing Systems
Type
Article
This Version
AM
Metadata
Show full item recordCitation
Drotár, P., Jamasb, A. R., Day, B., Cangea, C., & Liò, P. (2021). 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
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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