Imputation of Assay Bioactivity Data Using Deep Learning.
Accepted version
Peer-reviewed
Repository URI
Repository DOI
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Authors
Whitehead, TM https://orcid.org/0000-0002-1460-8979
Irwin, BWJ
Segall, MD https://orcid.org/0000-0002-2105-6535
Conduit, GJ
Abstract
We describe a novel deep learning neural network method and its application to impute assay pIC50 values. Unlike conventional machine learning approaches, this method is trained on sparse bioactivity data as input, typical of that found in public and commercial databases, enabling it to learn directly from correlations between activities measured in different assays. In two case studies on public domain data sets we show that the neural network method outperforms traditional quantitative structure-activity relationship (QSAR) models and other leading approaches. Furthermore, by focusing on only the most confident predictions the accuracy is increased to R2 > 0.9 using our method, as compared to R2 = 0.44 when reporting all predictions.
Description
Keywords
Biological Assay, Databases, Pharmaceutical, Deep Learning, Drug Discovery, Molecular Structure, Pharmaceutical Preparations, Quantitative Structure-Activity Relationship
Journal Title
J Chem Inf Model
Conference Name
Journal ISSN
1549-9596
1549-960X
1549-960X
Volume Title
59
Publisher
American Chemical Society (ACS)
Publisher DOI
Sponsorship
Royal Society (IMF130944)
The Royal Society (uf130122)
Royal Society (RGF/EA/180034)
The Royal Society (uf130122)
Royal Society (RGF/EA/180034)