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AM participated in the preparation of the article's structure, graphs, and reference list.
PubChem3D enables similarity of chemical structure 3-D conformers to augment the existing similarity of 2-D chemical structure graphs.
This allows the adaptation of methods from graph theory to structure graphs and their direct use with kernel learning algorithms.
Many graph kernels are general-purpose, some are suitable for structure graphs, and a few have been explicitly designed for them.
Matching long cycles in dense structure graphs may cause an unavoidable complexity explosion, which is a problem inherent to graph substructure matching.
The main tools developed in this paper could be use to tackle the general problem of QTL mapping of complex (sequences, 3D structure, graphs) phenotypic traits.
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Molecular graph kernels [3] are a recent development where kernels are defined directly on the molecular structure graph.
One problem of combinatorial fragmenters is how to choose the costs for cleaving edges (bonds) in the molecular structure graph.
The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules.
Then, the structure graph and a constrained tree can be established automatically for the target symbol, using the knowledge acquisition algorithm.
At approximately n = 0.6, the geometrical change of the non-dimensional flow rate structure graph is in agreement with the reduction in n.
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