Date/heure
Date(s) - 5 décembre 2014

Catégories Pas de Catégories

$Title : Graph kernels in Chemoinformatics.\n\nAbstract :\n\nChemoinformatics is the use of computer and informational techniques applied to a range of problems in the field of chemistry (Wikipedia). This broad subject includes various topics such as the drawing of molecules or the creation of databases of molecules accessible through web search engines. In this talk we will explore a much more restricted field devoted to the prediction of molecule’s properties through machine learning techniques. One of the main principle of chemoinformatics states that “two similar molecules should have similar properties”. The methods presented in this talk will thus be based on the design of similarity measures between molecules. Such similarity measures are encoded by graph kernels based on two important properties of molecules : 1) The types of their atoms and the type of bounds between these atoms and 2) the different cycles of molecules and their adjacency relationships. The use of graph kernels allows to combine graph similarity measures with usual machine learning methods. We will present some experiments and conclude this talk on some extensions of this work to other fields.[$