Olier, I, Sadawi, N, Bickerton, GRJ, Vanschoren, J, Grosan, C, Soldatova, LN and King, RD (2017) Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning, 107 (1). 285 - 311.

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Abstract

We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

Item Type: Article
Additional Information: © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords: meta-learning; algorithm selection; drug discovery; QSAR
Subjects: Q Science > Q Science (General)
R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Primary Care Health Sciences
Depositing User: Symplectic
Date Deposited: 10 Jan 2019 16:33
Last Modified: 18 Mar 2019 14:44
URI: http://eprints.keele.ac.uk/id/eprint/5670

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