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How artificial intelligence is changing drug discovery
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“AI is, for example, the major driver behind the current trend toward personalized medicine, with treatments being tailored to each patient's specific biological profile (Fleming, 2018;Lichfield, 2018).”
From Artificial Intelligence and Management: The Automation–Augmentation Paradox (2021) · cites this paper as Fleming, 2018
Experimental — accuracy not yet measuredIn the citing paper
“Many more examples are available [3,32,33,34,35,36,37], showing that AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [37]
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“The dashed lines represent observed values listed in Table 5, that is, 480 for α = 2 and 22 for α = 3 companies, such as Pfizer and Novartis, deployed AI systems for drug discovery and clinical trials by IBM Pharmatutor.org , Fleming (2018).”
MethodArtificial Intelligence-Enabled Prognosis Technologies for SARS-CoV-2/COVID-19
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“…(Liu et al. 2019), klinische Entscheidungsfindung (Shortliffe und Sepúlveda 2018), personalisierte Medizin (Schork 2019), klinische Forschung (Woo 2019), Arzneimittelentwicklung (Fleming 2018), Verwaltungsprozesse (Davenport und Kalakota 2019), und gesundheitliche Ungleichheiten (Chen et al. 2020).”
BackgroundClustered Problems and Machine Learning Methodologies: A New Approach
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Metadata
- Authors
- Fleming, N.
- Corresponding author
- Fleming, Nic
- Venue
- Nature
- Published
- 2018-05-30
- Volume
- 557
- Issue
- 7707
- Pages
- S55-S57
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2806834952
- Cited by
- 546
- References
- 0
- Citations by year
- 2026: 41 · 2025: 49 · 2024: 81 · 2023: 95 · 2022: 97 · 2021: 81 · 2020: 56 · 2019: 38 · 2018: 7
- FWCI
- 34.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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, machine learning, pharmaceuticals, computer-aided drug design
- MeSH terms
- Machine Learning, Algorithms, Amyotrophic Lateral Sclerosis, Animals, Artificial Intelligence, Automation, Drug Industry, Humans, Neoplasms, Pattern Recognition, Automated, Robotics, Efficiency, Organizational, Mice, Drug Discovery, Precision Medicine
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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