The Potential Applications of Artificial Intelligence in Drug Discovery and Development
Open accessAbstract
Development of a new dug is a very lengthy and highly expensive process since only preclinical, pharmacokinetic, pharmacodynamic and toxicological studies include a multiple of in silico, in vitro, in vivo experimentations that traditionally last several years. In the present review, we briefly report some examples that demonstrate the power of the computer-assisted drug discovery process with some examples that are published and revealing the successful applications of artificial intelligence (AI) technology on this vivid area. Besides, we address the situation of drug repositioning (repurposing) in clinical applications. Yet few success stories in this regard that provide us with a clear evidence that AI will reveal its great potential in accelerating effective new drug finding. AI accelerates drug repurposing and AI approaches are altogether necessary and inevitable tools in new medicine development. In spite of the fact that AI in drug development is still in its infancy, the advancements in AI and machine-learning (ML) algorithms have an unprecedented potential. The AI/ML solutions driven by pharmaceutical scientists, computer scientists, statisticians, physicians and others are increasingly working together in the processes of drug development and are adopting AI-based technologies for the rapid discovery of medicines. AI approaches, coupled with big data, are expected to substantially improve the effectiveness of drug repurposing and finding new drugs for various complex human diseases.
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AI-based methods can accelerate the drug discovery process and enable the development of more effective medications.
“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 [33]
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(2021) · doi:10.1021/acs
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The role of AI in drug discovery: challenges, opportunities, and strategies
Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690 · see citing works
- Authors
- Farghali, H., Kutinová Canová, N., & Arora, M.
- Affiliations
- Charles University (CZ)
- ORCID
- Venue
- Physiological Research
- Published
- 2021-12-30
- Volume
- 70
- Issue
- Suppl4
- Pages
- S715-S722
- ISSN
- 0862-8408 (ISSN-L)
- 1802-9973
- PMC
- PMC9054182
- OpenAlex
- W4214953648
- Cited by
- 72
- References
- 20
- Citations by year
- 2026: 15 · 2025: 28 · 2024: 11 · 2023: 14 · 2022: 4
- FWCI
- 3.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- Physiological Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Univerzita Karlova v Praze (SVV 260 523, PROGRESS Q25)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- artificial intelligence, drug discovery, drug development, drug repurposing, machine learning, big data, pharmaceutical industry
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Humans, Drug Discovery
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