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IMPROVED ALGORITHMS FOR ENUMERATING TREE-LIKE CHEMICAL GRAPHS WITH GIVEN PATH FREQUENCY

机译:改进了具有给定路径频率的树状化学图的算法

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This paper considers the problem of enumerating all non-isomorphic tree-like chemical graphs with given path frequency, where "tree-like" means that the graph can be viewed as a tree if multiple edges (i.e., edges with the same end points) and a benzene ring are treated as one edge and one vertex, respectively, and "path frequency" is a vector of the numbers of specified vertex-labeled paths that must be realized in every output. This and related problems have several potential applications such as classification of chemical compounds, structure determination using mass-spectrum and/or NMR and design of novel chemical compounds. For this problem, several studies have been done. Recently, Fujiwara et al. (2008) showed two formulations and for each of them, they gave a branch-and-bound algorithm, which combined efficient enumeration of non-isomorphic trees with bounding operations based on the path frequency and the atom-atom bonds to avoid the generation of invalid trees. In this paper, based on their work and a result of Nagamochi (2006), we introduce two new bounding operations, the detachment-cut and the Hcut, to further reduce the size of the search space. We performed computational experiments to compare our proposed algorithms with those of Fujiwara et al. (2008) using some chemical compound data obtained from the KEGG LIGAND database (http: //www.genome. jp/kegg/ligand.html). The results show that our proposed algorithms are much faster than their algorithms.
机译:本文考虑了给定路径频率枚举所有非同义树木等化学图的问题,其中“树状”意味着如果多个边缘(即,具有相同端点的边缘),则可以将图形视为树苯环分别作为一个边缘和一个顶点,“路径频率”是一个边缘,并且“路径频率”是指定顶点标记的路径的数量,必须在每个输出中实现。该和相关问题具有几种潜在的应用,例如化学化合物的分类,使用质谱和/或NMR的结构测定和新型化学化合物的设计。对于这个问题,已经完成了几项研究。最近,Fujiwara等。 (2008)显示了两种配方和它们中的每一个,它们提供了一种分支和绑定算法,其基于路径频率和原子原子键的边界操作组合了非同种型树的高效计量,以避免产生无效的树木。在本文的基础上,基于他们的工作和Nagamochi(2006)的结果,我们介绍了两个新的边界操作,分离和HCUT,以进一步降低搜索空间的大小。我们进行了计算实验,以将我们提出的算法与富士瓦拉等人的算法进行比较。 (2008)使用从Kegg配体数据库获得的一些化学化合物数据(http://www.genome。jp / kegg / ligand.html)。结果表明,我们的建议算法比其算法快得多。

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