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Sadawi, N, Olier, I, Vanschoren, J, van Rijn, JN, Besnard, J, Bickerton, R, Grosan, C, Soldatova, L and King, RD (2019) Multi-task learning with a natural metric for quantitative structure activity relationship learning. Journal of Cheminformatics, 11 (68). pp. 1-13. ISSN 1758-2946
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Abstract
The goal of Quantitative Structure Activity Relationship (QSAR) Learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays. We used datasets containing curated records about the activity of speci c compounds on drug targets provided by ChEMBL. Totally, 1091 assays have been analysed. As a baseline, a single task learning approach that trains random forest to predict drug activity for each drug target individually was considered. We then carried out feature-based and instance-based MTL to predict drug activities. We introduced a natural metric of evolutionary distance between drug targets as a measure of tasks relatedness. Instance-based MTL signi cantly outperformed both, feature-based MTL and the base learner, on 741 drug targets out of 1091. Feature-based MTL won on 179 occasions and the base learner performed best on 171 drug targets. We conclude that MTL QSAR is improved by incorporating the evolutionary distance between targets. These results indicate that QSAR learning can be performed effectively, even if little data is available for speci c drug targets, by leveraging what is known about similar drug targets.
| Item Type: | Article |
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| Uncontrolled Keywords: | Multi-Task Learning; Quantitative Structure Activity Relationship; Sequence-Based Similarity; Random Forest |
| Subjects: | Q Science > QA Mathematics |
| Divisions: | Applied Mathematics (merged with Comp Sci 10 Aug 20) |
| Publisher: | BioMed Central |
| Date Deposited: | 06 Nov 2019 11:45 |
| Last Modified: | 15 Aug 2022 12:45 |
| DOI or ID number: | 10.1186/s13321-019-0392-1 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/11710 |
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