Cited by 34Computational Drug Discovery MethodsReceptor Mechanisms and SignalingMachine Learning in Materials Science

The opportunities of mining historical and collective data in drug discovery

Wassermann, Anne Mai, Lounkine, Eugen, Davies, John W., Glick, Meir, Camargo, L. Miguel·2015·Drug Discovery Today

Abstract

Vast amounts of bioactivity data have been generated for small molecules across public and corporate domains. Biological signatures, either derived from systematic profiling efforts or from existing historical assay data, have been successfully employed for small molecule mechanism-of-action elucidation, drug repositioning, hit expansion and screening subset design. This article reviews different types of biological descriptors and applications, and we demonstrate how biological data can outlive the original purpose or project for which it was generated. By comparing 150 HTS campaigns run at Novartis over the past decade on the basis of their active and inactive chemical matter, we highlight the opportunities and challenges associated with cross-project learning in drug discovery.

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Crossref · 10.1016/j.drudis.2014.11.004

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97 of 97 references verified0 citation errors · 0 not checkable

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  • An enriched representation of molecules can be achieved through the use of bioactivity signatures.

    “Indeed, it has been shown that an enriched representation of molecules can be achieved through the use of bioactivity signatures3.”

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Authors
Wassermann, A. M., Lounkine, E., Davies, J. W., Glick, M., & Camargo, L. M.
Corresponding author
  • Wassermann, Anne Mai
  • Camargo, L. Miguel
Venue
Drug Discovery Today
Publisher
Elsevier BV
Published
2015-04
Volume
20
Issue
4
Pages
422-434
ISSN
1359-6446 (ISSN-L)
OpenAlex
W2038773870
Cited by
34
References
97
Citations by year
2025: 2 · 2024: 4 · 2023: 1 · 2022: 3 · 2021: 3 · 2020: 2 · 2019: 5 · 2018: 2 · 2017: 3 · 2016: 6 · 2015: 3
FWCI
0.33
Type
journal article
Language
en
Open access
closed
Locations
  • Drug Discovery Today (publishedVersion)
  • PubMed (publishedVersion)
Funders
Novartis
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Keywords
biological signatures, small molecules, mechanism of action, drug repurposing, hit expansion, high-throughput screening, cross-project learning, data reuse
MeSH terms
Animals, Computer Simulation, Pharmaceutical Preparations, Humans, Models, Molecular, Structure-Activity Relationship, Molecular Structure, Signal Transduction, History, 21st Century, Drug Discovery, Data Mining, Databases, Chemical, Databases, Pharmaceutical

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