Graph Kernels for Chemoinformatics
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چکیده
In chemoinformatics and bioinformatics, it is effective to automatically predict the properties of chemical compounds and proteins with computeraided methods, since this can substantially reduce the costs of research and development by screening out unlikely compounds and proteins from the candidates for ‘wet” experiment. Data-driven predictive modeling is one of the main research topics in chemoinformatics and bioinformatics. A chemical compound can be represented as a graph (Figure 1) by considering the atomic species (such as C, Cl, and H) as the vertex labels, and the bond types (such as s (single bond) and d (double bond)) as the edge labels. Similarly, chemical reactions can be analyzed as graphs where the chemical compounds and their relationships during the reaction are considered as vertices and edges, AbsTRACT
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تاریخ انتشار 2015