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Olier, I, Sadawi, N, Bickerton, GR, Vanschoren, J, Grosan, C, Soldatova, L and King, RD (2017) Meta-QSAR: a large-scale application of meta-learning to drug design and discovery. Machine Learning, 107 (1). pp. 285-311. ISSN 0885-6125
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Meta-QSAR a large-scale application of meta-learning to drug design and discovery.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
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 |
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| Uncontrolled Keywords: | 0801 Artificial Intelligence And Image Processing, 1702 Cognitive Science |
| Subjects: | Q Science > QA Mathematics R Medicine > RM Therapeutics. Pharmacology |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | Springer |
| Related URLs: | |
| Date Deposited: | 23 May 2018 08:56 |
| Last Modified: | 04 Sep 2021 10:28 |
| DOI or ID number: | 10.1007/s10994-017-5685-x |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/8700 |
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