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PLoS Comput Biol. 2019 Aug 5;15(8):e1006813. doi: 10.1371/journal.pcbi.1006813. eCollection 2019 Aug.

Predicting kinase inhibitors using bioactivity matrix derived informer sets.

Author information

1
Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
2
Small Molecule Screening Facility, Drug Development Core, UW-Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
3
Department of Mathematics and Institute for Mathematical Science, National University of Singapore, Singapore.
4
Department of Medicine, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
5
Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennesse, United States of America.
6
Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
7
Morgridge Institute for Research, Madison, Wisconsin, United States of America.
8
Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
9
Department of Oncology, McArdle Laboratory for Cancer Research, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.

Abstract

Prediction of compounds that are active against a desired biological target is a common step in drug discovery efforts. Virtual screening methods seek some active-enriched fraction of a library for experimental testing. Where data are too scarce to train supervised learning models for compound prioritization, initial screening must provide the necessary data. Commonly, such an initial library is selected on the basis of chemical diversity by some pseudo-random process (for example, the first few plates of a larger library) or by selecting an entire smaller library. These approaches may not produce a sufficient number or diversity of actives. An alternative approach is to select an informer set of screening compounds on the basis of chemogenomic information from previous testing of compounds against a large number of targets. We compare different ways of using chemogenomic data to choose a small informer set of compounds based on previously measured bioactivity data. We develop this Informer-Based-Ranking (IBR) approach using the Published Kinase Inhibitor Sets (PKIS) as the chemogenomic data to select the informer sets. We test the informer compounds on a target that is not part of the chemogenomic data, then predict the activity of the remaining compounds based on the experimental informer data and the chemogenomic data. Through new chemical screening experiments, we demonstrate the utility of IBR strategies in a prospective test on three kinase targets not included in the PKIS.

PMID:
31381559
PMCID:
PMC6695194
DOI:
10.1371/journal.pcbi.1006813
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Conflict of interest statement

The authors have declared that no competing interests exist.

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