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Finding Largest Common Substructures of Molecules in Quadratic Time

机译:在二次时间内找到分子的最大共同子结构

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Finding the common structural features of two molecules is a fundamental task in cheminformatics. Most drugs are small molecules, which can naturally be interpreted as graphs. Hence, the task is formalized as maximum common subgraph problem. Albeit the vast majority of molecules yields outerplanar graphs this problem remains NP-hard. We consider a variation of the problem of high practical relevance, where the rings of molecules must not be broken, i.e., the block and bridge structure of the input graphs must be retained by the common subgraph. We present an algorithm for finding a maximum common connected induced subgraph of two given outerplanar graphs subject to this constraint. Our approach runs in time O(∆n~2) in outerplanar graphs on n vertices with maximum degree ∆. This leads to a quadratic time complexity in molecular graphs, which have bounded degree. The experimental comparison on synthetic and real-world datasets shows that our approach is highly efficient in practice and outperforms comparable state-of-the-art algorithms.
机译:寻找两个分子的共同结构特征是化学信息学的基本任务。大多数药物都是小分子,可以自然地解释为图表。因此,该任务被形式化为最大公共子图问题。尽管绝大多数分子会产生外平面图,但此问题仍然是NP难题。我们考虑了高度实用相关性问题的一种变体,在这种变体中,分子的环一定不能折断,即输入图的块和桥结构必须由公共子图保留。我们提出了一种算法,用于找到受此约束的两个给定外平面图的最大公共连通诱导子图。我们的方法在最大度数为∆的n个顶点的外平面图中的时间O(∆n〜2)中运行。这导致分子图中的二次时间复杂度是有限的。在合成数据集和真实数据集上进行的实验比较表明,我们的方法在实践中非常高效,并且优于可比的最新算法。

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