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  4. Ligand affinity prediction with multi-pattern kernels
 
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2016
Conference Paper
Title

Ligand affinity prediction with multi-pattern kernels

Abstract
We consider the problem of affinity prediction for protein ligands. For this purpose, small molecule candidates can easily become regression algorithm inputs if they are represented as vectors indexed by a set of physico-chemical properties or structural features of their molecular graphs. There are plenty of so-called molecular fingerprints, each with a characteristic composition or generation of features. This raises the question which fingerprint to choose for a given learning task? In addition, none of the standard fingerprints, however, systematically gathers all circular and tree patterns independent of size and the adjacency information of atoms. Since structural and neighborhood information are crucial for the binding capacity of small molecules, we combine the features of existing graph kernels in a novel way such that finally both aspects are covered and the fingerprint choice is included in the learning process. More precisely, we apply the Weisfeiler-Lehman labeling algorithm to encode neighborhood information in the vertex labels. Based on the relabeled graphs we calculate four types of structural features: Cyclic and tree patterns, shortest paths and the Weisfeiler-Lehman labels. We combine these different views using different multi-view regression algorithms. Our experiments demonstrate that affinity prediction profits from the application of multiple views, outperforming state-of-the-art single fingerprint approaches.
Author(s)
Ullrich, Katrin  
Mack, J.
Welke, Pascal
Mainwork
Discovery science. 19th international conference, DS 2016  
Conference
International Conference on Discovery Science (DS) 2016  
DOI
10.1007/978-3-319-46307-0_30
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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