Cited by 140Computational Drug Discovery MethodsArtificial Intelligence in Healthcare and EducationMachine Learning in Materials Science

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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

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Koromina, Maria, Pandi, Maria-Theodora, Patrinos, George P.·2019·OMICS A Journal of Integrative Biology

DOI 10.1089/omi.2019.0151

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Abstract

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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Authors: Koromina, M., Pandi, M.-T., & Patrinos, G. P.
Affiliations: University of Patras (GR)
Corresponding author: Patrinos, George P.
ORCID: Koromina, Maria
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
DOI: 10.1089/omi.2019.0151
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
Licence: journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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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Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics

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