611
Views
17
CrossRef citations to date
0
Altmetric
Review

Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties

, , , , , & show all
Pages 169-181 | Received 20 Oct 2016, Accepted 03 Apr 2017, Published online: 23 Apr 2017

Keep up to date with the latest research on this topic with citation updates for this article.

Read on this site (3)

Priya Petchimuthu, Chandu Ala, Selvaraj Kunjiappan, Parasuraman Pavadai, Murugesan Sankaranarayanan, Sureshbabu Ram Kumar Pandian & Krishnan Sundar. (2023) Pharmacoinformatics-based identification of phytochemicals from Solanum torvum Swartz. fruits as potential inhibitors for MAPK14 protein. Journal of Biomolecular Structure and Dynamics 0:0, pages 1-17.
Read now
Rosemarie Brandim Marques, Maria Das Dores Barreto Sousa, Wesley de Sousa Santos, Neirigelson Ferreira de Barros Leite, Esdras Morais Sobreiro Lima, Angélica Lima Soares, Charllyton Luís Sena da Costa, Francisco Artur e Silva Filho, Antônio Luiz Martins Maia Filho, Evandro Paulo Soares Martins, Ricardo Martins Ramos & Antonio de Macedo Filho. (2023) Pharmacokinetic and toxicological prediction of the chemical constituents of the essential oil of the leaves of Croton heliotropiifolius Kunth. Journal of Toxicology and Environmental Health, Part A 86:14, pages 479-490.
Read now
Julia Pletz, Steven J. Enoch, Diviya M. Jais, Claire L. Mellor, Gopal Pawar, James W. Firman, Judith C. Madden, Steven D. Webb, Carlos A. Tagliati & Mark T. D. Cronin. (2018) A critical review of adverse effects to the kidney: mechanisms, data sources, and in silico tools to assist prediction. Expert Opinion on Drug Metabolism & Toxicology 14:12, pages 1225-1253.
Read now

Articles from other publishers (14)

Anita Rácz, Anna Vincze, Balázs Volk & György T. Balogh. (2023) Extending the limitations in the prediction of PAMPA permeability with machine learning algorithms. European Journal of Pharmaceutical Sciences 188, pages 106514.
Crossref
Haoliang Xiao & Xiangyang Chen. (2022) Drug ADMET Prediction Method Based on Improved Graph Convolution Neural Network. Drug ADMET Prediction Method Based on Improved Graph Convolution Neural Network.
Aldert H. Piersma, Nancy C. Baker, George P. Daston, Burkhard Flick, Michio Fujiwara, Thomas B. Knudsen, Horst Spielmann, Noriyuki Suzuki, Katya Tsaioun & Hajime Kojima. (2022) Pluripotent stem cell assays: Modalities and applications for predictive developmental toxicity. Current Research in Toxicology 3, pages 100074.
Crossref
Judith C. Madden & Courtney V. Thompson. 2022. In Silico Methods for Predicting Drug Toxicity. In Silico Methods for Predicting Drug Toxicity 57 83 .
James M. Armitage, Lauren Hughes, Alessandro Sangion & Jon A. Arnot. (2021) Development and intercomparison of single and multicompartment physiologically-based toxicokinetic models: Implications for model selection and tiered modeling frameworks. Environment International 154, pages 106557.
Crossref
Edward Price, J. Cory Kalvass, David DeGoey, Balakrishna Hosmane, Stella Doktor & Kelly Desino. (2021) Global Analysis of Models for Predicting Human Absorption: QSAR, In Vitro , and Preclinical Models . Journal of Medicinal Chemistry 64:13, pages 9389-9403.
Crossref
Nicholas Ball, Judith Madden, Alicia Paini, Miriam Mathea, Andrew David Palmer, Saskia Sperber, Thomas Hartung & Bennard van Ravenzwaay. (2020) Key read across framework components and biology based improvements. Mutation Research/Genetic Toxicology and Environmental Mutagenesis 853, pages 503172.
Crossref
Yuzhong Peng, Yanmei Lin, Xiao-Yuan Jing, Hao Zhang, Yiran Huang & Guang Sheng Luo. (2020) Enhanced Graph Isomorphism Network for Molecular ADMET Properties Prediction. IEEE Access 8, pages 168344-168360.
Crossref
Asish Mohapatra. 2020. Information Resources in Toxicology. Information Resources in Toxicology 791 812 .
Nichola Gellatly & Fiona Sewell. (2019) Regulatory acceptance of in silico approaches for the safety assessment of cosmetic-related substances. Computational Toxicology 11, pages 82-89.
Crossref
Judith C. Madden, Gopal Pawar, Mark T.D. Cronin, Steven Webb, Yu-Mei Tan & Alicia Paini. (2019) In silico resources to assist in the development and evaluation of physiologically-based kinetic models. Computational Toxicology 11, pages 33-49.
Crossref
Gopal Pawar, Judith C. Madden, David Ebbrell, James W. Firman & Mark T. D. Cronin. (2019) In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR. Frontiers in Pharmacology 10.
Crossref
Antonia Diukendjieva, Ivanka Tsakovska, Petko Alov, Tania Pencheva, Ilza Pajeva, Andrew P. Worth, Judith C. Madden & Mark T.D. Cronin. (2019) Advances in the prediction of gastrointestinal absorption: Quantitative Structure-Activity Relationship (QSAR) modelling of PAMPA permeability. Computational Toxicology 10, pages 51-59.
Crossref
Mukesh Patel, Martyn L. Chilton, Andrea Sartini, Laura Gibson, Chris Barber, Liz Covey-Crump, Katarzyna R. Przybylak, Mark T. D. Cronin & Judith C. Madden. (2018) Assessment and Reproducibility of Quantitative Structure–Activity Relationship Models by the Nonexpert. Journal of Chemical Information and Modeling 58:3, pages 673-682.
Crossref

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.