The DOI matches its Crossref record; 74 of the 74 references it cites were found in a registry or an index.
Artificial intelligence in drug development: present status and future prospects
Abstract
Artificial intelligence (AI) uses personified knowledge and learns from the solutions it produces to address not only specific but also complex problems. Remarkable improvements in computational power coupled with advancements in AI technology could be utilised to revolutionise the drug development process. At present, the pharmaceutical industry is facing challenges in sustaining their drug development programmes because of increased R&D costs and reduced efficiency. In this review, we discuss the major causes of attrition rates in new drug approvals, the possible ways that AI can improve the efficiency of the drug development process and collaboration of pharmaceutical industry giants with AI-powered drug discovery firms.
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“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.”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [36]
Citations
Cites
Artificial intelligence in healthcare: past, present and future
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Can the pharmaceutical industry reduce attrition rates?
Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167
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Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
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Cited by
Sorted by citations
Why 90% of clinical drug development fails and how to improve it?
Sun, D., Gao, W., Hu, H., & Zhou, S. (2022) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2022.02.002 · Cited by 1,763
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473
“Hence, there is a dire need for new drug targets and drug compounds, which can alleviate the symptoms and mitigate the diseased conditions of the central nervous systems [462].”
BackgroundArtificial Intelligence in Pharmaceutical Technology and Drug Delivery Design
Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,193
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Metadata
- Authors
- Mak, K.-K., & Pichika, M. R.
- Affiliations
- IMU University (MY)
- Corresponding author
- Pichika, Mallikarjuna Rao
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 24
- Issue
- 3
- Pages
- 773-780
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2901411193
- Cited by
- 1,018
- References
- 73
- Citations by year
- 2026: 161 · 2025: 196 · 2024: 190 · 2023: 170 · 2022: 99 · 2021: 119 · 2020: 63 · 2019: 18
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- Funders
- International Medical University
- 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 › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, drug development, drug discovery, attrition rates, research and development costs, pharmaceutical industry
- MeSH terms
- Artificial Intelligence, Drug Industry, Humans, Drug Discovery
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