A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application
Rattachement africain : in, us, gb. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
The development of artificial intelligence (AI) tools and technology has made AI-driven drug discovery a more prominent field. We are firmly in the AI era, with hybrid designs that eventually comprise deep learning (DL) and conventional machine learning (ML). Although traditional models can predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties, they remain relatively unsuccessful, and improving the accuracy of predictions remains challenging. Recently, several researchers have developed a hybrid learning model that successfully addresses these problems and improves prediction accuracy. The systematic tendencies facing AI-powered transformation from conventional DL and ML to hybrid learning AI models are examined in this review. Compared with traditional ML and DL, hybrid AI models have increased efficiency by reducing drug development time and costs, and improved success rates. In this context, the ongoing development of new ADMET software based on hybrid AI and multimodeling techniques can enhance the accuracy of pharmacokinetic-pharmacodynamic predictions, improve ADMET endpoint predictions, and expedite the drug discovery of new chemical entities. Moreover, this review covers the future of AI in pharmaceutical sciences and ADMET predictions, including AI-driven prediction models that range from basic ML/DL to newly developed hybrid models, evaluation parameters, and their applications in ADMET property prediction. SIGNIFICANCE STATEMENT: The article covers the compilation of ongoing research in the development of ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) software based on hybrid artificial intelligence and multimodeling techniques, which may increase the accuracy of pharmacokinetic-pharmacodynamic predictions, improve ADMET endpoint predictions, and accelerate drug discovery.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application
- Date Crossref
- 01/03/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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National Institute of Pharmaceutical Education and Research Department of Pharmaceutical Analysis pays non établi dans la noticeUniversité ou école supérieure
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Johnson & Johnson (United States) pays non établi dans la noticeEntreprise
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Certara (United States) pays non établi dans la noticeEntreprise
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AstraZeneca (United Kingdom) pays non établi dans la noticeEntreprise
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Indian Institute of Technology Kharagpur pays non établi dans la noticeUniversité ou école supérieure
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University of Bradford pays non établi dans la noticeUniversité ou école supérieure
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Translational PK/PD and Investigative Toxicology pays non établi dans la noticeInstitution
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Certara Inc Simcyp Division pays non établi dans la noticeEntreprise
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Research and Development pays non établi dans la noticeInstitution
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Augmented DMTA Platform pays non établi dans la noticeInstitution
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School of Medical Science and Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Pharmacy and Medical Sciences Institute of Cancer Therapeutics pays non établi dans la noticeUniversité ou école supérieure
Department of Pharmaceutical Analysis — National Institute of Pharmaceutical Education and Research, Johnson & Johnson (United States) et Certara (United States), avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.