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Multiple Kernel Learning for Drug Discovery
Author(s) -
Pilkington Nicholas C. V.,
Trotter Matthew W. B.,
Holden Sean B.
Publication year - 2012
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201100146
Subject(s) - multiple kernel learning , weighting , artificial intelligence , support vector machine , kernel (algebra) , computer science , classifier (uml) , pattern recognition (psychology) , machine learning , kernel method , property (philosophy) , data mining , mathematics , medicine , combinatorics , radiology , philosophy , epistemology
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.