Taking into account stereoisomerism in the prediction of molecular properties

Pierre-Anthony Grenier 1, 2 Luc Brun 2, 1 Didier Villemin 2, 3
1 Equipe Image - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : The prediction of molecule's properties through Quantitative Structure Activity (resp. Property) Relationships are two active research fields named QSAR and QSPR. Within these frameworks Graph kernels allow to combine a natural encoding of a molecule by a graph with classical statistical tools such as SVM or kernel ridge regression. Unfortunately some molecules encoded by a same graph and differing only by the three dimensional orientation of their atoms in space have different properties. Such molecules are called stereoisomers. These latter properties can not be predicted by usual graph methods which do not encode stereoisomerism. In a previous paper, we proposed to encode the stereoiso-merism property of each atom by a local subgraph, called minimal stereo subgraph, and we designed a kernel based on the comparison of bags of such subgraphs. However, the encoding of a molecule by a bag of subgraphs induces an important loss of information. In this paper, we propose a new kernel based both on the spatial relationships between minimal stereo subgraphs and the local neighbourhood of each minimal stereo subgraph within its supergraph. Our experiments show the benefits of taking into account such information.
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Communication dans un congrès
23rd International Conference on Pattern Recognition (ICPR), Dec 2016, Cancun, Mexico. IEEE, pp.1543-1548, 2016, 23rd International Conference on Pattern Recognition (ICPR). 〈http://www.icpr2016.org/site/〉
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Dernière modification le : jeudi 7 février 2019 - 17:40:27

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Pierre-Anthony Grenier, Luc Brun, Didier Villemin. Taking into account stereoisomerism in the prediction of molecular properties. 23rd International Conference on Pattern Recognition (ICPR), Dec 2016, Cancun, Mexico. IEEE, pp.1543-1548, 2016, 23rd International Conference on Pattern Recognition (ICPR). 〈http://www.icpr2016.org/site/〉. 〈hal-01418939〉

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