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Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions

Jack Scantlebury, Nathan Brown, View ORCID ProfileFrank Von Delft, View ORCID ProfileCharlotte M. Deane
doi: https://doi.org/10.1101/2020.03.06.979625
Jack Scantlebury
†Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
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Nathan Brown
‡BenevolentAI, 4-8 Maple St, London, W1T 5HD, UK
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Frank Von Delft
¶Structural Genomics Consortium (SGC), University of Oxford, Oxford, OX3 7DQ, UK
§Diamond Light Source, Harwell Science and Innovation Campus, Didcot OX11 0DE, UK
‖Department of Biochemistry, University of Johannesburg, Aukland Park, Johannesburg 2006, South Africa
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Charlotte M. Deane
†Department of Statistics, University of Oxford, 24-29 St Giles, Oxford, OX1 3LB, UK
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  • ORCID record for Charlotte M. Deane
  • For correspondence: deane@stats.ox.ac.uk
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Abstract

Current deep learning methods for structure-based virtual screening take the structures of both the protein and the ligand as input but make little or no use of the protein structure when predicting ligand binding. Here we show how a relatively simple method of dataset augmentation forces such deep learning methods to take into account information from the protein. Models trained in this way are more generalisable (make better predictions on protein-ligand complexes from a different distribution to the training data). They also assign more meaningful importance to the protein and ligand atoms involved in binding. Overall, our results show that dataset augmentation can help deep learning based virtual screening to learn physical interactions rather than dataset biases.

Footnotes

  • ↵* E-mail: jack.scantlebury{at}hertford.ox.ac.uk; nathan.brown{at}benevolent.ai; frank.vondelft{at}sgc.ox.ac.uk

  • http://dude.docking.org/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions
Jack Scantlebury, Nathan Brown, Frank Von Delft, Charlotte M. Deane
bioRxiv 2020.03.06.979625; doi: https://doi.org/10.1101/2020.03.06.979625
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Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalise To Unseen Target Classes, And Highlight Important Binding Interactions
Jack Scantlebury, Nathan Brown, Frank Von Delft, Charlotte M. Deane
bioRxiv 2020.03.06.979625; doi: https://doi.org/10.1101/2020.03.06.979625

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