title: Multiple Kernel Learning for Drug Discovery.
abstract: The support vector machine (SVM) methodology has become a popular and well-used component of present chemometric analysis. We assess a relatively recent development of the algorithm, multiple kernel learning (MKL), on published structure-property relationship (SPR) data. The MKL algorithm learns a weighting across multiple kernel-based representations of the data during supervised classifier creation and, thereby, may be used to describe the influence of distinct groups of structural descriptors upon a single structure-property classifier without explicitly omitting any of them. We observe a statistically significant performance improvement over a conventional, single kernel SVM on all three SPR data sets analysed. Furthermore, MKL output is observed to provide useful information regarding the relative influence of five distinct descriptor subsets present in each data set.
authors.affiliation: University of Cambridge Computer Laboratory, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK phone: +44 (0)1223 763725.
authors.forename: Nicholas C V
authors.initials: NC
authors.lastname: Pilkington
authors.affiliation: Anne McLaren Laboratory for Regenerative Medicine & Department of Surgery, University of Cambridge, UK.|Celgene Institute for Translational Research Europe (CITRE), Sevilla, Spain.
authors.forename: Matthew W B
authors.initials: MW
authors.lastname: Trotter
authors.affiliation: University of Cambridge Computer Laboratory, 15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK phone: +44 (0)1223 763725. Sean.Holden@cl.cam.ac.uk.
authors.forename: Sean B
authors.initials: SB
authors.lastname: Holden
keywords: Chemoinformatics; Drug discovery; Kernel methods; Machine learning; Structure-property relationships