Cited by 455Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

Big data and artificial intelligence modeling for drug discovery

Open access
Zhu, Hao·2020·Annual Review of Pharmacology and Toxicology

Abstract

Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.

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Crossref · 10.1146/annurev-pharmtox-010919-023324

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  • Machine learning and artificial intelligence have shown promise in overcoming some hindrances to multi-omics integration.

    “Machine learning (ML) and artificial intelligence (AI) have shown promise in overcoming some of these hindrances (e.g., Mirza et al., 2019; Zhu, 2020) and have gained popularity in recent years.”
  • AI can distinguish hit and lead compounds, allowing for faster therapeutic target validation and structural design optimization.

    “AI can distinguish hit and lead compounds, allowing for faster therapeutic target validation and structural design optimization.”

    From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Zhu 2020

  • Despite its benefits, AI has substantial data difficulties, including its scale, growth, diversity, and ambiguity.

    “Despite its benefits, AI has substantial data difficulties, including its scale, growth, diversity, and ambiguity”

    From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Zhu 2020

  • AI can be used effectively in different parts of drug discovery, including drug design, chemical synthesis, drug screening, polypharmacology, and drug repurposing.

    “AI can be used effectively in different parts of drug discovery, including drug design, chemical synthesis, drug screening, polypharmacology, and drug repurposing.”

    From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Zhu 2020

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Research works printed but not linked: 2

  1. [28]Broach JR, Thorner J. 1996 High-throughput screening for drug discovery. Nature 384: 14– 16 8895594
  2. [101]Hinton G, Deng L, Yu D, Dahl GE, Mohamed AR, 2012 Deep neural networks for acoustic modeling in speech recognition. IEEE Signal. Proc. Mag 29: 82– 97

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Authors
Zhu, H.
Affiliations
Rutgers, The State University of New Jersey (US)
Corresponding author
Zhu, Hao
Venue
Annual Review of Pharmacology and Toxicology
Published
2020-01-06
Volume
60
Issue
1
Pages
573-589
ISSN
  • 0362-1642 (ISSN-L)
  • 1545-4304
OpenAlex
W2972497960
Cited by
455
References
155
Citations by year
2026: 43 · 2025: 64 · 2024: 112 · 2023: 95 · 2022: 59 · 2021: 60 · 2020: 21
FWCI
2.91
Type
journal article
Language
en
Open access
Locations
  • The Annual Review of Pharmacology and Toxicology (publishedVersion)
  • PubMed (publishedVersion)
  • Europe PMC (PubMed Central) (acceptedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Environmental Health Sciences (R15ES023148)
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
drug discovery, big data, artificial intelligence, deep learning, drug safety prediction, data mining, data curation, data management, structure-activity relationship, rational drug design
MeSH terms
Drug Development, Deep Learning, Big Data, Animals, Artificial Intelligence, Humans, Models, Theoretical, Drug Discovery, Data Mining

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