The DOI matches its Crossref record; 251 of the 252 references it cites were found in a registry or an index.
Automating drug discovery
Abstract
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.
Overview
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Citations
Cites
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1 of the 252 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Sparkes, A. et al. Towards robot scientists for autonomous scientific discovery. Autom. Exp. 2, 1 (2010). · DOI
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Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
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On the Opportunities and Risks of Foundation Models
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MethodClosed-loop optimization of fast-charging protocols for batteries with machine learning
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“Adaptive OED algorithms have been successfully applied to physical science domains, such as materials science 1,2,12–14 , chemistry 15,16 , biology 17 and drug discovery 18 , as well as to computer science domains, such as hyperparameter optimization for machine learning 19,20 .”
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Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V. L., Hart, T., Fishman, J., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,133
“Synthesis is often a bottleneck in small-molecule drug discovery ( 1 ), where design – synthesize – test iter- ationshavecycletimesontheorderofweeksandwherethescopeofacompoundlibrarysynthe-sis can determine the accuracy of an empirical structure – activity relationship model ( 2 ).”
Background
Metadata
- Authors
- Schneider, G.
- Affiliations
- ETH Zurich (CH)
- Corresponding author
- Schneider, Gisbert
- ORCID
- Schneider, Gisbert
- Venue
- Nature Reviews Drug Discovery
- Published
- 2017-12-15
- Volume
- 17
- Issue
- 2
- Pages
- 97-113
- ISSN
- 1474-1776 (ISSN-L)
- 1474-1784
- OpenAlex
- W2775714759
- Cited by
- 910
- References
- 252
- Citations by year
- 2026: 167 · 2025: 154 · 2024: 119 · 2023: 89 · 2022: 87 · 2021: 92 · 2020: 79 · 2019: 74 · 2018: 47 · 2017: 1
- FWCI
- 5.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Reviews Drug Discovery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- AstraZeneca
- OPO-Stiftung
- European Commission (HORIZON2020, 675555, 676434)
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (157190, 159737)
- Horizon 2020 (676434)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
- Keywords
- small molecule drug discovery, artificial intelligence in drug design, compound optimization, chemical space exploration, medicinal chemistry, pharmacokinetics, drug safety
- MeSH terms
- Animals, Drug Industry, Pharmaceutical Preparations, Humans, Drug Discovery
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