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Title: Predicting kinase inhibitors using bioactivity matrix derived informer sets

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 informermore » 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.« less

Authors:
 [1]; ORCiD logo [1];  [2];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [3]; ORCiD logo [4];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1];  [1]
+ Show Author Affiliations
  1. Univ. of Wisconsin, Madison, WI (United States)
  2. National Univ. of Singapore (Singapore)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. Univ. of Wisconsin, Madison, WI (United States); Morgridge Inst. for Research, Madison, WI (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE; National Science Foundation (NSF)
OSTI Identifier:
1559708
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
PLoS Computational Biology (Online)
Additional Journal Information:
Journal Name: PLoS Computational Biology (Online); Journal Volume: 15; Journal Issue: 8; Journal ID: ISSN 1553-7358
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Zhang, Huikun, Ericksen, Spencer S., Lee, Ching-pei, Ananiev, Gene E., Wlodarchak, Nathan, Yu, Peng, Mitchell, Julie C., Gitter, Anthony, Wright, Stephen J., Hoffmann, F. Michael, Wildman, Scott A., Newton, Michael A., and Schlessinger, Avner. Predicting kinase inhibitors using bioactivity matrix derived informer sets. United States: N. p., 2019. Web. doi:10.1371/journal.pcbi.1006813.
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Zhang, Huikun, Ericksen, Spencer S., Lee, Ching-pei, Ananiev, Gene E., Wlodarchak, Nathan, Yu, Peng, Mitchell, Julie C., Gitter, Anthony, Wright, Stephen J., Hoffmann, F. Michael, Wildman, Scott A., Newton, Michael A., & Schlessinger, Avner. Predicting kinase inhibitors using bioactivity matrix derived informer sets. United States. https://doi.org/10.1371/journal.pcbi.1006813
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Zhang, Huikun, Ericksen, Spencer S., Lee, Ching-pei, Ananiev, Gene E., Wlodarchak, Nathan, Yu, Peng, Mitchell, Julie C., Gitter, Anthony, Wright, Stephen J., Hoffmann, F. Michael, Wildman, Scott A., Newton, Michael A., and Schlessinger, Avner. Mon . "Predicting kinase inhibitors using bioactivity matrix derived informer sets". United States. https://doi.org/10.1371/journal.pcbi.1006813. https://www.osti.gov/servlets/purl/1559708.
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@article{osti_1559708,
title = {Predicting kinase inhibitors using bioactivity matrix derived informer sets},
author = {Zhang, Huikun and Ericksen, Spencer S. and Lee, Ching-pei and Ananiev, Gene E. and Wlodarchak, Nathan and Yu, Peng and Mitchell, Julie C. and Gitter, Anthony and Wright, Stephen J. and Hoffmann, F. Michael and Wildman, Scott A. and Newton, Michael A. and Schlessinger, Avner},
abstractNote = {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.},
doi = {10.1371/journal.pcbi.1006813},
journal = {PLoS Computational Biology (Online)},
number = 8,
volume = 15,
place = {United States},
year = {Mon Aug 05 00:00:00 EDT 2019},
month = {Mon Aug 05 00:00:00 EDT 2019}
}
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https://doi.org/10.1371/journal.pcbi.1006813
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Experimental Design Strategy: Weak Reinforcement Leads to Increased Hit Rates and Enhanced Chemical Diversity
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Public Domain HTS Fingerprints: Design and Evaluation of Compound Bioactivity Profiles from PubChem’s Bioassay Repository
journal, January 2016

  • Helal, Kazi Yasin; Maciejewski, Mateusz; Gregori-Puigjané, Elisabet
  • Journal of Chemical Information and Modeling, Vol. 56, Issue 2
  • DOI: 10.1021/acs.jcim.5b00498

Data-Driven Derivation of an “Informer Compound Set” for Improved Selection of Active Compounds in High-Throughput Screening
journal, August 2016

  • Paricharak, Shardul; IJzerman, Adriaan P.; Jenkins, Jeremy L.
  • Journal of Chemical Information and Modeling, Vol. 56, Issue 9
  • DOI: 10.1021/acs.jcim.6b00244

Predicted Biological Activity of Purchasable Chemical Space
journal, December 2017

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Analysis of Iterative Screening with Stepwise Compound Selection Based on Novartis In-house HTS Data
journal, February 2016

  • Paricharak, Shardul; IJzerman, Adriaan P.; Bender, Andreas
  • ACS Chemical Biology, Vol. 11, Issue 5
  • DOI: 10.1021/acschembio.6b00029

Aggregated Compound Biological Signatures Facilitate Phenotypic Drug Discovery and Target Elucidation
journal, September 2016

  • Cortes Cabrera, Alvaro; Lucena-Agell, Daniel; Redondo-Horcajo, Mariano
  • ACS Chemical Biology, Vol. 11, Issue 11
  • DOI: 10.1021/acschembio.6b00358

Extended-Connectivity Fingerprints
journal, April 2010

  • Rogers, David; Hahn, Mathew
  • Journal of Chemical Information and Modeling, Vol. 50, Issue 5
  • DOI: 10.1021/ci100050t

Predicting Drug–Target Interactions Using Probabilistic Matrix Factorization
journal, December 2013

  • Cobanoglu, Murat Can; Liu, Chang; Hu, Feizhuo
  • Journal of Chemical Information and Modeling, Vol. 53, Issue 12
  • DOI: 10.1021/ci400219z

Using Information from Historical High-Throughput Screens to Predict Active Compounds
journal, June 2014

  • Riniker, Sereina; Wang, Yuan; Jenkins, Jeremy L.
  • Journal of Chemical Information and Modeling, Vol. 54, Issue 7
  • DOI: 10.1021/ci500190p

Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects
journal, September 2009

  • Wassermann, Anne Mai; Geppert, Hanna; Bajorath, Jürgen
  • Journal of Chemical Information and Modeling, Vol. 49, Issue 10
  • DOI: 10.1021/ci9002624

Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation
journal, January 2010

  • Geppert, Hanna; Vogt, Martin; Bajorath, Jürgen
  • Journal of Chemical Information and Modeling, Vol. 50, Issue 2
  • DOI: 10.1021/ci900419k

Do Structurally Similar Molecules Have Similar Biological Activity?
journal, September 2002

  • Martin, Yvonne C.; Kofron, James L.; Traphagen, Linda M.
  • Journal of Medicinal Chemistry, Vol. 45, Issue 19
  • DOI: 10.1021/jm020155c

The Properties of Known Drugs. 1. Molecular Frameworks
journal, January 1996

  • Bemis, Guy W.; Murcko, Mark A.
  • Journal of Medicinal Chemistry, Vol. 39, Issue 15
  • DOI: 10.1021/jm9602928

Docking and scoring in virtual screening for drug discovery: methods and applications
journal, November 2004

  • Kitchen, Douglas B.; Decornez, Hélène; Furr, John R.
  • Nature Reviews Drug Discovery, Vol. 3, Issue 11
  • DOI: 10.1038/nrd1549

Model selection and estimation in regression with grouped variables
journal, February 2006

Computational Methods in Drug Discovery
journal, December 2013

  • Sliwoski, Gregory; Kothiwale, Sandeepkumar; Meiler, Jens
  • Pharmacological Reviews, Vol. 66, Issue 1
  • DOI: 10.1124/pr.112.007336

Connecting Small Molecules with Similar Assay Performance Profiles Leads to New Biological Hypotheses
journal, January 2014

  • Dančík, Vlado; Carrel, Hyman; Bodycombe, Nicole E.
  • Journal of Biomolecular Screening, Vol. 19, Issue 5
  • DOI: 10.1177/1087057113520226

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
journal, August 2017

Comprehensive characterization of the Published Kinase Inhibitor Set
text, January 2016

  • A., Tropsha,; M., Kunkel,; X. -P., Huang,
  • Nature Publishing Group
  • DOI: 10.17615/fnn8-qq87

Regularization Paths for Generalized Linear Models via Coordinate Descent
journal, January 2010

  • Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert
  • Journal of Statistical Software, Vol. 33, Issue 1
  • DOI: 10.18637/jss.v033.i01

Chemogenomic Data Analysis: Prediction of Small-Molecule Targets and the Advent of Biological Fingerprints
journal, September 2007

  • Bender, Andreas; Young, Daniel; Jenkins, Jeremy
  • Combinatorial Chemistry & High Throughput Screening, Vol. 10, Issue 8
  • DOI: 10.2174/138620707782507313

Seeding Collaborations to Advance Kinase Science with the GSK Published Kinase Inhibitor Set (PKIS)
journal, January 2014

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances
journal, October 2014

Active learning for computational chemogenomics
text, January 2017

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