Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Ivan Olier, Noureddin Sadawi, G. Richard Bickerton, Joaquin Vanschoren, Crina Grosan (Lead / Corresponding author), Larisa Soldatova, Ross D. King

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
154 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)285-311
Number of pages27
JournalMachine Learning
Volume107
Issue number1
Early online date22 Dec 2017
DOIs
Publication statusPublished - Jan 2018

Keywords

  • Algorithm selection
  • Drug discovery
  • Meta-learning
  • QSAR

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