The opportunities of mining historical and collective data in drug discovery
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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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.”
From Bioactivity descriptors for uncharacterized chemical compounds (2021) · cites this paper as [3]
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Bioactivity descriptors for uncharacterized compoundsPreprint
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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