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

By Iván Olier, Noureddin M. Sadawi, G. R. Bickerton, Joaquin Vanschoren, Crina Grosan, Larisa N. Soldatova, Ross D. King. 2017

87 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Spectroscopy

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Citations in the corpus

Sever: A Robust Meta-Algorithm for Stochastic Optimization (2018, methodology): “The drug discovery dataset was obtained from the ChEMBL database and was originally curated by [OSB18]; it consists of 4084 data points in 410 dimensions; we split this into a training set of 3084 points and a test set of 1000 points.”
Tilted Empirical Risk Minimization (2020, methodology): “, 2019) which is originally curated from (Olier et al., 2018) and train linear regression models with different losses.”