Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
- Submitting institution
-
Liverpool John Moores University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 1021
- Type
- D - Journal article
- DOI
-
10.1007/s10994-017-5685-x
- Title of journal
- Machine Learning
- Article number
- -
- First page
- 285
- Volume
- 107
- Issue
- 1
- ISSN
- 0885-6125
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
6
- Research group(s)
-
-
- Citation count
- 19
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work models the quantitative structure-activity relationships (QSARs) domain, by automatically performing machine learning experiments on over 10,000 datasets. The results are presented as a case-study of meta-learning. All datasets and models were published and are freely accessible. Available online since 22/Dec/2017, the work has been downloaded more than 3,000 times and cited 13 times. It summarises the main results of the EPSRC-funded project EP/K030469/1 (£401K, 2013-2015).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
