Cited by 920Computational Drug Discovery MethodsMachine Learning in Materials ScienceChemical Synthesis and Analysis

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Automating drug discovery

Schneider, Gisbert·2018·Nature Reviews Drug Discovery

DOI 10.1038/nrd.2017.232

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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.

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Authors: Schneider, G.
Affiliations: ETH Zurich (CH)
Corresponding author: Schneider, Gisbert
ORCID: Schneider, Gisbert
Venue: Nature Reviews Drug Discovery
Publisher: Springer Science and Business Media LLC
Published: 2017-12-15
Volume: 17
Issue: 2
Pages: 97-113
ISSN: 1474-1776 (ISSN-L)
1474-1784
DOI: 10.1038/nrd.2017.232
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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