National audienceGraph 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 graph kernel) to predict the optical rotation of molecules
International audienceChemoinformatics is a research field concerned with the study of physical or b...
The Shape Group method is a powerful tool in the analysis of the shape of molecules, and in the corr...
Molecular similarity measures are important for many cheminformatics applications like ligand-based ...
National audienceGraph kernels provides a framework combining machine learning and graph theory. How...
International audienceMolecules being often described using a graph representation, graph kernels pr...
International audienceThe prediction of molecule's properties through Quantitative Structure Activit...
Molecules being often described using a graph representation, graph kernels provide an interesting f...
International audienceAn important field of chemoinformatics consists in the prediction of molecule'...
International audienceThe prediction of molecule's properties through Quantitative Structure Activit...
An important field of chemoinformatics consists in the pre-diction of molecule's properties, and wit...
In the framework of prediction of molecule’s properties, graph kernels allow to combine anatural enc...
In computer-aided drug discovery, quantitative structure activity relation models are trained to pre...
International audienceQuantitative Structure Activity and Property Relationships (QSAR and QSPR), ai...
International audienceChemoinformatics is a research field concerned with the study of physical or b...
The Shape Group method is a powerful tool in the analysis of the shape of molecules, and in the corr...
Molecular similarity measures are important for many cheminformatics applications like ligand-based ...
National audienceGraph kernels provides a framework combining machine learning and graph theory. How...
International audienceMolecules being often described using a graph representation, graph kernels pr...
International audienceThe prediction of molecule's properties through Quantitative Structure Activit...
Molecules being often described using a graph representation, graph kernels provide an interesting f...
International audienceAn important field of chemoinformatics consists in the prediction of molecule'...
International audienceThe prediction of molecule's properties through Quantitative Structure Activit...
An important field of chemoinformatics consists in the pre-diction of molecule's properties, and wit...
In the framework of prediction of molecule’s properties, graph kernels allow to combine anatural enc...
In computer-aided drug discovery, quantitative structure activity relation models are trained to pre...
International audienceQuantitative Structure Activity and Property Relationships (QSAR and QSPR), ai...
International audienceChemoinformatics is a research field concerned with the study of physical or b...
The Shape Group method is a powerful tool in the analysis of the shape of molecules, and in the corr...
Molecular similarity measures are important for many cheminformatics applications like ligand-based ...