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Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalize 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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Posted March 15, 2020.
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Dataset Augmentation Allows Deep Learning-Based Virtual Screening To Better Generalize 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 Generalize 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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