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Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU-based disease: the Multi-Targeting Drug DREAM Challenge

Zhaoping Xiong, View ORCID ProfileMinji Jeon, View ORCID ProfileRobert J Allaway, View ORCID ProfileJaewoo Kang, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, Miyoung Ko, Hualiang Jiang, View ORCID ProfileMingyue Zheng, View ORCID ProfileAik Choon Tan, View ORCID ProfileXindi Guo, The Multi-Targeting Drug DREAM Challenge Community, View ORCID ProfileKristen K Dang, View ORCID ProfileAlex Tropsha, Chana Hecht, View ORCID ProfileTirtha K. Das, View ORCID ProfileHeather A. Carlson, View ORCID ProfileRuben Abagyan, View ORCID ProfileJustin Guinney, View ORCID ProfileAvner Schlessinger, View ORCID ProfileRoss Cagan
doi: https://doi.org/10.1101/2021.02.15.430538
Zhaoping Xiong
5Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai 200031, China
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Minji Jeon
2Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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Robert J Allaway
1Sage Bionetworks, Seattle, WA
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Jaewoo Kang
2Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
3Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
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Donghyeon Park
2Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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Jinhyuk Lee
2Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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Hwisang Jeon
3Interdisciplinary Graduate Program in Bioinformatics, Korea University, Seoul, Republic of Korea
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Miyoung Ko
2Department of Computer Science and Engineering, Korea University, Seoul, Republic of Korea
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Hualiang Jiang
6Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
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Mingyue Zheng
6Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China
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Aik Choon Tan
4Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL, USA
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Xindi Guo
1Sage Bionetworks, Seattle, WA
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Kristen K Dang
1Sage Bionetworks, Seattle, WA
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Alex Tropsha
7Laboratory for Molecular Modeling, Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, USA
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Chana Hecht
11Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Tirtha K. Das
11Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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Heather A. Carlson
8Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI, USA
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Ruben Abagyan
9Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, San Diego, CA, USA
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Justin Guinney
1Sage Bionetworks, Seattle, WA
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Avner Schlessinger
10Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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  • For correspondence: ross.cagan@glasgow.ac.uk avner.schlessinger@mssm.edu
Ross Cagan
11Department of Cell, Developmental, and Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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  • For correspondence: ross.cagan@glasgow.ac.uk
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Abstract

A continuing challenge in modern medicine is the identification of safer and more efficacious drugs. Precision therapeutics, which have one molecular target, have been long promised to be safer and more effective than traditional therapies. This approach has proven to be challenging for multiple reasons including lack of efficacy, rapidly acquired drug resistance, and narrow patient eligibility criteria. An alternative approach is the development of drugs that address the overall disease network by targeting multiple biological targets (‘polypharmacology’). Rational development of these molecules will require improved methods for predicting single chemical structures that target multiple drug targets. To address this need, we developed the Multi-Targeting Drug DREAM Challenge, in which we challenged participants to predict single chemical entities that target pro-targets but avoid anti-targets for two unrelated diseases: RET-based tumors and a common form of inherited Tauopathy. Here, we report the results of this DREAM Challenge and the development of two neural network-based machine learning approaches that were applied to the challenge of rational polypharmacology. Together, these platforms provide a potentially useful first step towards developing lead therapeutic compounds that address disease complexity through rational polypharmacology.

Author Summary Many modern drugs are developed with the goal of modulating a single cellular pathway or target. However, many drugs are, in fact, ‘dirty;’ they target multiple cellular pathways or targets. This phenomenon is known as multi-targeting or polypharmacology. While some strive to develop ‘cleaner’ therapeutics that eliminate secondary targets, recent work has shown that multi-targeting therapeutics have key advantages for a variety of diseases. However, while multi-targeting drugs that affect a precisely-defined profile of targets may be more effective, it is difficult to computationally predict which molecules have desirable target profiles. Here, we report the results of a competitive crowdsourcing project (the Multi-Targeting Drug DREAM Challenge), where we challenged participants to predict chemicals that have desired target profiles for cancer and neurodegenerative disease.

Competing Interest Statement

AS is co-founder of AIchemy, LLC. RC is a consultant to My Personal Therapeutics/ Vivan.

Footnotes

  • ↵* co-first authors

  • fix typo in author’s name, adding new COI

  • https://www.doi.org/10.7303/syn8404040

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 03, 2021.
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Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU-based disease: the Multi-Targeting Drug DREAM Challenge
Zhaoping Xiong, Minji Jeon, Robert J Allaway, Jaewoo Kang, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, Miyoung Ko, Hualiang Jiang, Mingyue Zheng, Aik Choon Tan, Xindi Guo, The Multi-Targeting Drug DREAM Challenge Community, Kristen K Dang, Alex Tropsha, Chana Hecht, Tirtha K. Das, Heather A. Carlson, Ruben Abagyan, Justin Guinney, Avner Schlessinger, Ross Cagan
bioRxiv 2021.02.15.430538; doi: https://doi.org/10.1101/2021.02.15.430538
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Crowdsourced identification of multi-target kinase inhibitors for RET- and TAU-based disease: the Multi-Targeting Drug DREAM Challenge
Zhaoping Xiong, Minji Jeon, Robert J Allaway, Jaewoo Kang, Donghyeon Park, Jinhyuk Lee, Hwisang Jeon, Miyoung Ko, Hualiang Jiang, Mingyue Zheng, Aik Choon Tan, Xindi Guo, The Multi-Targeting Drug DREAM Challenge Community, Kristen K Dang, Alex Tropsha, Chana Hecht, Tirtha K. Das, Heather A. Carlson, Ruben Abagyan, Justin Guinney, Avner Schlessinger, Ross Cagan
bioRxiv 2021.02.15.430538; doi: https://doi.org/10.1101/2021.02.15.430538

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