Journal article
Protein family-specific models using deep neural networks and transfer learning improve virtual screening and highlight the need for more data
- Abstract:
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Machine learning has shown enormous potential for computer-aided drug discovery. Here we show how modern convolutional neural networks (CNNs) can be applied to structure-based virtual screening. We have coupled our densely connected CNN (DenseNet) with a transfer learning approach which we use to produce an ensemble of protein family-specific models. We conduct an in-depth empirical study and provide the first guidelines on the minimum requirements for adopting a protein family-specific model...
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- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 3.3MB, Terms of use)
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- Publisher copy:
- 10.1021/acs.jcim.8b00350
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Funding
Bibliographic Details
- Publisher:
- American Chemical Society
- Journal:
- Journal of Chemical Information and Modeling More from this journal
- Volume:
- 58
- Issue:
- 11
- Pages:
- 2319–2330
- Publication date:
- 2018-10-01
- Acceptance date:
- 2018-10-01
- DOI:
- EISSN:
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1549-960X
- ISSN:
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1549-9596
Item Description
Terms of use
- Copyright holder:
- American Chemical Society
- Copyright date:
- 2018
- Notes:
- Copyright © 2018 American Chemical Society. This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited.
- Licence:
- CC Attribution (CC BY)
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