Department of Information Systems and Mathematical Sciences, Nanzan University , Seto 489-0863, Japan.
J Chem Inf Model. 2014 Apr 28;54(4):1027-35. doi: 10.1021/ci400601c. Epub 2014 Apr 9.
Computer-assisted chemical structure elucidation has been intensively studied since the first use of computers in chemistry in the 1960s. Most of the existing elucidators use a structure-spectrum database to obtain clues about the correct structure. Such a structure-spectrum database is expected to grow on a daily basis. Hence, the necessity to develop an efficient structure elucidation system that can adapt to the growth of a database has been also growing. Therefore, we have developed a new elucidator using practically efficient graph algorithms, including the convex bipartite matching, weighted bipartite matching, and Bron-Kerbosch maximal clique algorithms. The utilization of the two matching algorithms especially is a novel point of our elucidator. Because of these sophisticated algorithms, the elucidator exactly produces a correct structure if all of the fragments are included in the database. Even if not all of the fragments are in the database, the elucidator proposes relevant substructures that can help chemists to identify the actual chemical structures. The elucidator, called the CAST/CNMR Structure Elucidator, plays a complementary role to the CAST/CNMR Chemical Shift Predictor, and together these two functions can be used to analyze the structures of organic compounds.
自 20 世纪 60 年代计算机首次应用于化学领域以来，人们一直在深入研究计算机辅助化学结构解析。大多数现有的解析器都使用结构-光谱数据库来获取有关正确结构的线索。这样的结构-光谱数据库预计每天都会增长。因此，开发一种能够适应数据库增长的高效结构解析系统的必要性也在不断增加。因此，我们使用实用的高效图算法（包括凸二分匹配、加权二分匹配和 Bron-Kerbosch 最大团算法）开发了一种新的解析器。这两个匹配算法的使用是我们的解析器的一个新特点。由于这些复杂的算法，如果数据库中包含所有的片段，那么解析器就能准确地生成正确的结构。即使不是所有的片段都在数据库中，解析器也会提出相关的子结构，帮助化学家识别实际的化学结构。该解析器名为 CAST/CNMR 结构解析器，与 CAST/CNMR 化学位移预测器互补，这两个功能可用于分析有机化合物的结构。
