Timezone: »
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.
Author Information
Pavol Drotar (University of Cambridge)
Arian Jamasb (University of Cambridge)
Ben Day (University of Cambridge)
Catalina Cangea (University of Cambridge)
Pietro Lió (University of Cambridge)
Related Events (a corresponding poster, oral, or spotlight)
-
2021 : Structure-aware generation of drug-like molecules »
Mon. Dec 13th 03:10 -- 03:20 PM Room None
More from the Same Authors
-
2021 : Interpretable Data Analysis for Bench-to-Bedside Research »
Zohreh Shams · Botty Dimanov · Nikola Simidjievski · Helena Andres-Terre · Paul Scherer · Urška Matjašec · Mateja Jamnik · Pietro Lió -
2021 : 3D Pre-training improves GNNs for Molecular Property Prediction »
Hannes Stärk · Dominique Beaini · Gabriele Corso · Prudencio Tossou · Christian Dallago · Stephan Günnemann · Pietro Lió -
2021 : 3D Pre-training improves GNNs for Molecular Property Prediction »
Hannes Stärk · Gabriele Corso · Christian Dallago · Stephan Günnemann · Pietro Lió -
2021 : Approximate Latent Force Model Inference »
Jacob Moss · Felix Opolka · Pietro Lió -
2021 : Neural ODE Processes: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Jacob Moss · Pietro Lió -
2021 : On Second Order Behaviour in Augmented Neural ODEs: A Short Summary »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2020 Poster: Constraining Variational Inference with Geometric Jensen-Shannon Divergence »
Jacob Deasy · Nikola Simidjievski · Pietro Lió -
2020 Poster: On Second Order Behaviour in Augmented Neural ODEs »
Alexander Norcliffe · Cristian Bodnar · Ben Day · Nikola Simidjievski · Pietro Lió -
2019 : Catered Lunch and Poster Viewing (in Workshop Room) »
Gustavo Stolovitzky · Prabhu Pradhan · Pablo Duboue · Zhiwen Tang · Aleksei Natekin · Elizabeth Bondi · Xavier Bouthillier · Stephanie Milani · Heimo Müller · Andreas T. Holzinger · Stefan Harrer · Ben Day · Andrey Ustyuzhanin · William Guss · Mahtab Mirmomeni