The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
ADMET modeling approaches in drug discovery
Abstract
In silico prediction of ADMET is an important component of pharmaceutical R&D. Last year, the FDA approved 59 new molecular entities, with small molecules comprising 64% of the therapies approved in 2018. Estimation of pharmacokinetic properties in the early phases of drug discovery has been central to guiding hit-to-lead and lead-optimization efforts. Given the outstanding complexity of the current R&D model, drug discovery players have intensely pursued molecular modeling strategies to identify patterns in ADMET data and convert them into knowledge. The field has advanced alongside the progress of chemoinformatics, which has evolved from traditional chemometrics to advanced machine learning methods.
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Cites
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From machine learning to deep learning: progress in machine intelligence for rational drug discovery
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Addressing the Metabolic Stability of Antituberculars through Machine Learning
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Cited by
Khan, T., Ali, M., Khan, A., Nisar, P., Jan, S. A., Afridi, S., & Shinwari, Z. K. (2019) · Biomolecules · doi:10.3390/biom10010047 · Cited by 375
Blockchain and Artificial Intelligence Technology in E-Health
Tagde, P., Tagde, S., Bhattacharya, T., Tagde, P., Chopra, H., Akter, R., Kaushik, D., & Rahman, M. H. (2021) · Environmental Science and Pollution Research · doi:10.1007/s11356-021-16223-0 · Cited by 281
Explainable artificial intelligence for drug discovery and development: a comprehensive survey
Alizadehsani, R., Oyelere, S. S., Hussain, S., Jagatheesaperumal, S. K., Calixto, R. R., Rahouti, M., Roshanzamir, M., & De Albuquerque, V. H. C. (2024) · IEEE Access · doi:10.1109/access.2024.3373195 · Cited by 124
Success stories of AI in drug discovery - where do things stand?
Mak, K.-K., Balijepalli, M. K., & Pichika, M. R. (2022) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.1985108 · Cited by 51
Large Language Model Agent for Modular Task Execution in Drug Discovery
Ock, J., Meda, R. S., Badrinarayanan, S., Aluru, N. S., Chandrasekhar, A., & Barati Farimani, A. (2026) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c02454 · Cited by 5
Metadata
- Authors
- Ferreira, L. L. G., & Andricopulo, A. D.
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 24
- Issue
- 5
- Pages
- 1157-1165
- ISSN
- 1359-6446 (ISSN-L)
- OpenAlex
- W2920835902
- Cited by
- 825
- References
- 10
- FWCI
- 35.36
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
