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Méthodes d'extraction de connaissances à partir de données modélisables par des graphes : Application à des problèmes de synthèse organique

Abstract : Millions of chemical reactions are described in databases as transformations of molecular graphs. This thesis proposes different data-mining methods to extract relevant patterns included in those graphs and therefore to help chemists in improving knowledge about chemical reactions and molecules. One first shows how the central problem of searching frequent reaction patterns can be solved using existing graph-mining methods. Introducing the general model of most informative patterns then allows experts to reduce the analysis of these frequent patterns to a very small set of non-redundant patterns characteristic of data. If the application of this model to reaction database identifies large and characteristic families of reactions, the model doesn't allow in practice the extraction of reaction patterns characteristic of synthesis methods (abbr. CSM patterns) as their frequencies are far too low. In order to overcome this problem, is introduced a heuristic search algorithm based on a graph interval constraint and able to extract patterns with very low frequency. Thus this method determines from examples of chemical reactions and under some conditions the CSM pattern underlying a given input reaction. The same approach is then used to address the problem of supervised classification of vertices or edges based on their environment and then applied to evaluate formability of bonds in molecules. Experimental results have been analyzed by experts and are very encouraging.
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Frédéric Pennerath. Méthodes d'extraction de connaissances à partir de données modélisables par des graphes : Application à des problèmes de synthèse organique. Autre [cs.OH]. Université Henri Poincaré - Nancy 1, 2009. Français. ⟨NNT : 2009NAN10061⟩. ⟨tel-01748457⟩

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