Meiler J, Will M
Institute of Organic Chemistry, Marie-Curie-Strasse 11, Universität Frankfurt, D-60439 Frankfurt, Germany.
J Chem Inf Comput Sci. 2001 Nov-Dec;41(6):1535-46. doi: 10.1021/ci0102970.
The automated structure elucidation of organic molecules from experimentally obtained properties is extended by an entirely new approach. A genetic algorithm is implemented that uses molecular constitution structures as individuals. With this approach, the structure of organic molecules can be optimized to meet experimental criteria, if in addition a fast and accurate method for the prediction of the used physical or chemical features is available. This is demonstrated using (13)C NMR spectrum as readily obtainable information. (13)C NMR chemical shift, intensity, and multiplicity information is available from (13)C NMR DEPT spectra. By means of artificial neural networks a fast and accurate method for calculating the (13)C NMR spectrum of the generated structures exists. The approach is limited by the size of the constitutional space that has to be searched and by the accuracy of the shift prediction for the unknown substance. The method is implemented and tested successfully for organic molecules with up to 20 non-hydrogen atoms.
一种全新的方法扩展了从实验获得的性质对有机分子进行自动结构解析的技术。实现了一种以分子组成结构作为个体的遗传算法。通过这种方法,如果有一种快速且准确的用于预测所使用的物理或化学特征的方法,那么有机分子的结构就可以被优化以符合实验标准。这通过使用¹³C NMR谱作为容易获得的信息得以证明。¹³C NMR化学位移、强度和多重性信息可从¹³C NMR DEPT谱中获取。借助人工神经网络,存在一种用于计算所生成结构的¹³C NMR谱的快速且准确的方法。该方法受到必须搜索的组成空间大小以及未知物质化学位移预测准确性的限制。该方法已成功实现并针对含有多达20个非氢原子的有机分子进行了测试。