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Cited by 319Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

AI in drug discovery and its clinical relevance

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Qureshi, Rizwan, Irfan, Muhammad, Gondal, Taimoor Muzaffar, Khan, Sheheryar, Wu, Jia, Hadi, Muhammad Usman, Heymach, John, Le, Xiuning +2 more·2023·Heliyon
DOI 10.1016/j.heliyon.2023.e17575PMC PMC10302550
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Abstract

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.

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  • In other settings, the same underlying AI technologies are used to advance medical drug discovery, including the development of Covid vaccines, enable real-time translation of spoken languages and provide free, customized tutoring to struggling learners and precocious autodidacts.

    “In other settings, the same underlying AI technologies are used to advance medical drug discovery (including the development of Covid vaccines) (Qureshi et al., 2023;Dolgin, 2023), enable real-time translation of spoken languages and provide free, customized tutoring to struggling learners and precocious autodidacts.”

    From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Qureshi et al., 2023

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Cited by

Sorted by citations

Authors
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T.
Affiliations
  • The University of Texas MD Anderson Cancer Center (US)
  • Ghulam Ishaq Khan Institute of Engineering Sciences and Technology (PK)
  • Superior University (PK)
  • Hong Kong Polytechnic University (HK)
  • University of Ulster (GB)
  • City University of Hong Kong (HK)
  • Hamad bin Khalifa University (QA)
Corresponding author
  • Qureshi, Rizwan
  • Alam, Tanvir
ORCID
Venue
Heliyon
Publisher
Elsevier BV
Published
2023-07
Volume
9
Issue
7
Pages
e17575
ISSN
2405-8440 (ISSN-L)
DOI
10.1016/j.heliyon.2023.e17575
PMC
PMC10302550
OpenAlex
W4382132796
Cited by
319
References
18
Citations by year
2026: 80 · 2025: 113 · 2024: 97 · 2023: 29
FWCI
5.20
Type
journal article
Language
en
Open access
Licence
Locations
  • Heliyon (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Ulster University Research Portal (Ulster University) (acceptedVersion, other-oa)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by)
  • Digital Commons-TMC (Texas Medical Center) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • CityU Scholars (submittedVersion, cc-by)
Funders
  • American Society of Clinical Oncology
  • Innovation and Technology Commission - Hong Kong
  • College of Science and Engineering, University of Minnesota
  • Qatar National Research Fund (RRC02-0805-210019, TDF 03-1206-210011)
  • Hamad Bin Khalifa University
  • Qatar National Library
  • National Cancer Institute
  • Research Grants Council, University Grants Committee (11204821)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › 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 in drug discovery, drug discovery pipeline, de novo drug design, molecular property prediction, graph neural networks, reinforcement learning, generative models, molecular docking, molecular dynamics simulation, structure-based drug design

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