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.
journal_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Whitehead TM,Irwin BWJ,Hunt P,Segall MD,Conduit GJdoi
10.1021/acs.jcim.8b00768subject
Has Abstractpub_date
2019-03-25 00:00:00pages
1197-1204issue
3eissn
1549-9596issn
1549-960Xjournal_volume
59pub_type
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