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Przybylak, KR, Madden, JC, Covey-Crump, E, Gibson, L, Barber, C, Patel, M and Cronin, MTD (2017) Characterisation of data resources for in silico modelling: benchmark datasets for ADME properties. Expert Opinion on Drug Metabolism and Toxicology. ISSN 1744-7607
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Characterisation of data resources for in silico modelling benchmark datasets for ADME properties..pdf - Accepted Version Download (454kB) | Preview |
Abstract
Introduction: The cost of in vivo and in vitro screening of ADME properties of compounds has motivated efforts to develop a range of in silico models. At the heart of the development of any computational model are the data; high quality data are essential for developing robust and accurate models. The characteristics of a dataset, such as its availability, size, format and type of chemical identifiers used, influence the modelability of the data. Areas covered: This review explores the usefulness of publicly available ADME datasets for researchers to use in the development of predictive models. More than 140 ADME datasets were collated from publicly available resources and the modelability of 31selected datasets were assessed using specific criteria derived in this study. Expert opinion: Publicly available datasets differ significantly in information content and presentation. From a modelling perspective, datasets should be of adequate size, available in a user-friendly format with all chemical structures associated with one or more chemical identifiers suitable for automated processing (e.g. CAS number, SMILES string or InChIKey). Recommendations for assessing dataset suitability for modelling and publishing data in an appropriate format are discussed.
| Item Type: | Article |
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| Additional Information: | This is an Accepted Manuscript of an article published by Taylor & Francis in Expert Opinion on Drug Metabolism and Toxicology on 23rd April 2017, available online: http://www.tandfonline.com/10.1080/17425255.2017.1316449 |
| Uncontrolled Keywords: | 1115 Pharmacology And Pharmaceutical Sciences |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software R Medicine > RS Pharmacy and materia medica |
| Divisions: | Pharmacy & Biomolecular Sciences |
| Publisher: | Taylor & Francis |
| Date Deposited: | 04 Apr 2017 09:18 |
| Last Modified: | 04 Sep 2021 04:01 |
| DOI or ID number: | 10.1080/17425255.2017.1316449 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/6207 |
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