Meta-QSAR: Learning How to Learn QSARs

Quantitative structure activity relationships (QSARs) are functions that predict bioactivity from compound structure. Although almost every form of statistical and machine learning method has been applied to learning QSARs, there is no single best way of learning QSARs. Therefore, currently the QSAR scientist has little to guide her/him on which QSAR approach to choose for a specific problem. The aim of this work is to introduce Meta-QSAR, a meta-learning approach aimed to learning which QSAR method is most appropriate for a particular problem. For the preliminary results presented here, we used ChEMBL, a public available chemoinformatic database, to systematically run extensive comparative QSAR experiments. We further apply meta-learning in order to generalise these results.