Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters-Reference-Cited by-同舟云学术

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Utilizing public and private sector data to build better machine learning models for the prediction of pharmacokinetic parameters

Published:2022-11 Issue:11 Volume:27 Page:103339
ISSN:1359-6446
Container-title:Drug Discovery Today
language:en
Short-container-title:Drug Discovery Today

Author:

Kuroda Masataka,Watanabe Reiko,Esaki Tsuyoshi,Kawashima Hitoshi,Ohashi Rikiya,Sato Tomohiro,Honma Teruki,Komura Hiroshi,Mizuguchi Kenji

Funder

Japan Agency for Medical Research and Development

Publisher

Elsevier BV

Subject

Drug Discovery,Pharmacology

Reference55 articles.

1. A decade of machine learning-based predictive models for human pharmacokinetics: advances and challenges;Danishuddin;Drug Discov Today,2022

2. Bayer's in silico ADMET platform: a journey of machine learning over the past two decades;Göller;Drug Discov Today,2020

3. Improving the accuracy of predicted human pharmacokinetics: lessons learned from the AstraZeneca drug pipeline over two decades;Davies;Trends Pharmacol Sci,2020

4. Computational model to predict the fraction of unbound drug in the brain;Esaki;J Chem Inf Model,2019

5. The European innovative medicines initiative: progress to date;Faure;Pharm Med,2018

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1. The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery;Pharmaceutics;2023-11-12

2. A Review on the Recent Applications of Deep Learning in Predictive Drug Toxicological Studies;Chemical Research in Toxicology;2023-08-10

3. Potential neonatal toxicity of new psychoactive substances;Pharmacology & Therapeutics;2023-08

4. DruMAP: A Novel Drug Metabolism and Pharmacokinetics Analysis Platform;Journal of Medicinal Chemistry;2023-07-14

5. Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study;Molecular Diversity;2023-04-06

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