Pierre-Anthony Grenier

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Molecules being often described using a graph representation , graph kernels provide an interesting framework which allows to combine machine learning and graph theory in order to predict molecule's properties. However, some of these properties are induced both by relationships between the atoms of a molecule and by constraints on the relative positioning(More)
Molecules being often described using a graph representation , graph kernels provide an interesting framework which allows to combine machine learning and graph theory in order to predict molecule's properties. However, some of these properties are induced both by the covalent bound relationships between atoms and by constraints on the relative positioning(More)
Chemoinformatics is a research field concerned with the study of physical or biological molecular properties through computer science's research fields such as machine learning and graph theory. From this point of view, graph kernels provide a nice framework which allows to naturally combine machine learning and graph theory techniques. Graph kernels based(More)
Graph kernels provides a framework combining machine learning and graph theory. However, kernels based upon the molecular graph, which can not distinguish stereoisomers, are unable to predict properties which differs among stereoisomers. This article presents a graph kernel which takes into account chirality, and is used (in combination with a classical(More)
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