Imputation of Assay Bioactivity Data Using Deep Learning.

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 Model

authors

Whitehead TM,Irwin BWJ,Hunt P,Segall MD,Conduit GJ

doi

10.1021/acs.jcim.8b00768

subject

Has Abstract

pub_date

2019-03-25 00:00:00

pages

1197-1204

issue

3

eissn

1549-9596

issn

1549-960X

journal_volume

59

pub_type

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