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

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

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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Crossref · 10.1089/omi.2019.0151

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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.”

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  1. Zhao K, and So H. (2017). A machine learning approach to drug repositioning based on drug expression profiles: Applications to schizophrenia and depression/anxiety disorders.arXiv

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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
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
Locations
  • OMICS A Journal of Integrative Biology (publishedVersion)
  • PubMed (publishedVersion)
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
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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