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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics
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Abstract Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and machine learning algorithms have slowly but surely begun to revolutionize pharmaceutical industry and drug development over the past 5 years. In this expert review, we describe the most frequently used machine learning algorithms in drug development pipelines and the -omics databases well poised to support machine learning and drug discovery. Subsequently, we analyze the emerging new computational approaches to drug discovery and the in silico pipelines for drug repositioning and the synergies among -omics system sciences, AI and machine learning. As with system sciences, AI and machine learning embody a system scale and Big Data driven vision for drug discovery and development. We conclude with a future outlook on the ways in which machine learning approaches can be implemented to buttress and expedite drug discovery and precision medicine. As AI and machine learning are rapidly entering pharmaceutical industry and the art and science of drug development, we need to critically examine the attendant prospects and challenges to benefit patients and public health.
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“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 [35]
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Metadata
- Authors
- Koromina, M., Pandi, M.-T., & Patrinos, G. P.
- Affiliations
- University of Patras (GR)
- Corresponding author
- Patrinos, George P.
- Venue
- OMICS A Journal of Integrative Biology
- Publisher
- SAGE Publications
- Published
- 2019-11
- Volume
- 23
- Issue
- 11
- Pages
- 539-548
- ISSN
- 1536-2310 (ISSN-L)
- 1557-8100
- OpenAlex
- W2981458781
- Cited by
- 140
- References
- 74
- Citations by year
- 2026: 14 · 2025: 28 · 2024: 25 · 2023: 23 · 2022: 21 · 2021: 16 · 2020: 13
- FWCI
- 8.49
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- OMICS A Journal of Integrative Biology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › 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
- drug repurposing, drug development, artificial intelligence, machine learning, omics databases, computational drug discovery, precision medicine, big data, pharmaceutical industry
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Models, Theoretical, Reproducibility of Results, Computational Biology, Genomics, Drug Discovery, Drug Repositioning
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