Applicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions-Reference-Cited by-同舟云学术

Applicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions

Published:2014-05-13 Issue:5 Volume:54 Page:1500-1511
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Carrió Pau¹,Pinto Marta²,Ecker Gerhard²,Sanz Ferran¹,Pastor Manuel¹

Affiliation:

1. Research Programme on Biomedical Informatics (GRIB), Department of Experimental and Health Sciences, Universitat Pompeu Fabra, IMIM (Hospital del Mar Medical Research Institute), Dr. Aiguader, 88, E-08003 Barcelona, Spain

2. Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Vienna, Austria

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci500172z

Reference40 articles.

1. The future of drug safety testing: expanding the view and narrowing the focus

2. The value of in silico chemistry in the safety assessment of chemicals in the consumer goods and pharmaceutical industries

3. REACH. European Community Regulation on chemicals and their safe use,http://ec.europa.eu/environment/chemicals/reach/reach_intro.htm(accessed Sept. 9, 2013) .

4. Improving Drug Candidates by Design: A Focus on Physicochemical Properties As a Means of Improving Compound Disposition and Safety

5. Exploratory drug safety: A discovery strategy to reduce attrition in development

Cited by 51 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advancing Ionic Liquid Research with pSCNN: A Novel Approach for Accurate Normal Melting Temperature Predictions;ACS Omega;2024-07-08

2. Chemoinformatic regression methods and their applicability domain;Molecular Informatics;2024-05-28

3. Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery;ChemElectroChem;2024-02-09

4. A practical guide to machine-learning scoring for structure-based virtual screening;Nature Protocols;2023-10-16

5. Uncertainty quantification: Can we trust artificial intelligence in drug discovery?;iScience;2022-08