The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
Machine Learning in Drug Discovery: A Review
Open accessAbstract
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
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AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183
Generative machine learning for de novo drug discovery: A systematic review
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Metadata
- Authors
- Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J.
- Venue
- Artificial Intelligence Review
- Published
- 2021-08-11
- Volume
- 55
- Issue
- 3
- Pages
- 1947-1999
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W3189831819
- Cited by
- 792
- References
- 42
- FWCI
- 53.36
- Type
- journal article
- Language
- en
- Open access
- green
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