Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery
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The development of a new drug is an expensive process with estimated costs of US $1.5 billion which can take several years, even before extensive clinical or preclinical testing [1]. Early-stage dr...
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The closed-loop approach does not need to create large libraries, rather employs agile experimental infrastructure, and statistics and ML to build QSAR-like models to predict the target properties for every element of the self-driving laboratory.
“It does not need to create large libraries, rather employs agile experimental infrastructure, and statistics and ML to build QSAR-like models to predict the target properties for every element of the self-driving laboratory.167”
From QSAR without borders (2020) · cites this paper as [167]
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- Authors
- Saikin, S. K., Kreisbeck, C., Sheberla, D., Becker, J. S., & Aspuru-Guzik, A.
- Affiliations
- Harvard University (US)
- Kebotix (United States) (US)
- Kebotix Inc., Cambridge, MA, USA
- Corresponding author
- Saikin, Semion K.
- Venue
- Expert Opinion on Drug Discovery
- Publisher
- Informa UK Limited
- Published
- 2018-11-29
- Volume
- 14
- Issue
- 1
- Pages
- 1-4
- ISSN
- 1746-0441 (ISSN-L)
- 1746-045X
- OpenAlex
- W2902568946
- Cited by
- 63
- References
- 23
- Citations by year
- 2026: 10 · 2025: 6 · 2024: 8 · 2023: 8 · 2022: 10 · 2021: 5 · 2020: 6 · 2019: 10
- FWCI
- 4.11
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-09-18)
- Open access
- bronze
- doi.org
- Locations
- Expert Opinion on Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Tata Sons Limited - Alliance Agreement
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in drug discovery, early drug development
- MeSH terms
- Machine Learning, Drug Development, Artificial Intelligence, Humans, Drug Discovery
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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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