Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data
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'Artificial Intelligence' (AI) has recently had a profound impact on areas such as image and speech recognition, and this progress has already translated into practical applications. However, in the drug discovery field, such advances remains scarce, and one of the reasons is intrinsic to the data used. In this review, we discuss aspects of, and differences in, data from different domains, namely the image, speech, chemical, and biological domains, the amounts of data available, and how relevant they are to drug discovery. Improvements in the future are needed with respect to our understanding of biological systems, and the subsequent generation of practically relevant data in sufficient quantities, to truly advance the field of AI in drug discovery, to enable the discovery of novel chemistry, with novel modes of action, which shows desirable efficacy and safety in the clinic.
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The impact of big data and AI in drug discovery continues to attract significant interest from investors, industrial and academic scientists, and legislators.
“The impact of big data and AI in drug discovery, the subject of a 2020 review in this journal (4), continues to attract significant interest: Investors (5, 6), industrial (7, 8) and academic scientists (9, 10), and legislators (https://bit.ly/40LRAip) discuss the impact of AI for drug discovery (AI4DD).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [10]
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Bender, A., & Cortés-Ciriano, I. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.12.009 · Cited by 405 · see citing works
Self-citationArtificial Intelligence for Drug Discovery: Are We There Yet?
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“The impact of big data and AI in drug discovery, the subject of a 2020 review in this journal (4), continues to attract significant interest: Investors (5, 6), industrial (7, 8) and academic scientists (9, 10), and legislators (https://bit.”
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Success stories of AI in drug discovery - where do things stand?
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- Authors
- Bender, A., & Cortes-Ciriano, I.
- Affiliations
- AstraZeneca (United Kingdom) (GB)
- Corresponding author
- Bender, Andreas
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 26
- Issue
- 4
- Pages
- 1040-1052
- ISSN
- 1359-6446 (ISSN-L)
- PMC
- PMC8132984
- OpenAlex
- W3125539506
- Cited by
- 232
- References
- 61
- Citations by year
- 2026: 52 · 2025: 49 · 2024: 43 · 2023: 46 · 2022: 28 · 2021: 13 · 2020: 1
- FWCI
- 2.70
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- European Molecular Biology Laboratory
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence, drug discovery, chemical information, biological data, data availability, data quality, machine learning, drug safety and efficacy
- MeSH terms
- Translational Science, Biomedical, Artificial Intelligence, Humans, Biomedical Research, Knowledge Bases, Drug Discovery
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