Big data and artificial intelligence modeling for drug discovery
Open accessAbstract
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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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.”
From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Zhu, 2020
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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- Authors
- Zhu, H.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Zhu, Hao
- ORCID
- Zhu, Hao
- Venue
- Annual Review of Pharmacology and Toxicology
- Publisher
- Annual Reviews
- Published
- 2020-01-06
- Volume
- 60
- Issue
- 1
- Pages
- 573-589
- ISSN
- 0362-1642 (ISSN-L)
- 1545-4304
- PMC
- PMC7010403
- 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
- green
- europepmc.org
- 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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