化学结构图草图预测的混合启发式搜索

Bo Kang, Hao Hu, J. Laviola
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引用次数: 1

摘要

速写是一种输入化学结构的自然方式,可用于从大型化学结构数据库中查询信息。基于用户不完整的化学结构草图,草图预测成为一个具有挑战性的问题,这不仅是因为用户之间的图纸顺序随意,而且化学结构布局之间也存在相似性。在本文中,我们提出了一种基于图的方法来处理草图预测问题。我们使用多集作为手绘化学结构的数据表示,并创建一个无向图来处理所有多集中的数据。该方法将草图预测问题转化为在相应子图中寻找具有多项式时间复杂度的哈密顿路径的搜索问题。我们引入混合启发式来指导搜索过程。通过对手绘化学结构数据集的初步实验,我们证明了与基线方法相比,该方法提高了预测精度,并且仅从部分草图中有效地预测化学结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mixed heuristic search for sketch prediction on chemical structure drawing
Sketching is a natural way to input chemical structures that can be used to query information from a large chemical structure database. Based on a user's incomplete sketch of a chemical structure, sketch prediction becomes a challenging problem not only due to arbitrary drawings orders among users but also similarities among chemical structure layouts. In this paper, we present a graph-based approach to handle the sketch prediction problem. We use multisets as the data representation of hand-drawn chemical structures and create an undirected graph to handle data in all multisets. This approach transforms the sketch prediction problem into a search problem to find a hamiltonian path in the corresponding sub-graph with polynomial time complexity. We introduce mixed heuristics to guide the search procedure. Through an initial experiment on a hand-drawn chemical structure dataset, we demonstrate that in comparison with a baseline method, the proposed approach improves the prediction accuracy and efficiently predicts chemical structures from only partially sketched drawings.
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