Abstract
The prediction of the biological activity of a chemical compound is a challenging task in Computational Chemistry and was restricted to vectorial representations of the molecular graph for decades. Kernel functions are positive semidefinite similarity measures that can be defined on arbitrary structured data. This class of similarity functions can be used in kernel-based machine learning algorithms. Interestingly, many graph kernel approaches from Computer Science share properties of traditional similarity measures for chemical compounds, like molecular fingerprints based on paths, cycles and subgraphs.
Highlights
4th German Conference on Chemoinformatics: 22
The prediction of the biological activity of a chemical compound is a challenging task in Computational Chemistry and was restricted to vectorial representations of the molecular graph for decades
Kernel functions are positive semidefinite similarity measures that can be defined on arbitrary structured data
Summary
4th German Conference on Chemoinformatics: 22. CIC-Workshop Frank Oellien Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1752-153X-3-S1-info.pdf . Address: University of Tübingen, Sand 1, 72076 Germany * Corresponding author from 4th German Conference on Chemoinformatics Goslar, Germany.
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