Zhang Qing-You, Carrera Gonçalo, Gomes Mário J S, Aires-de-Sousa João
Departamento de Química, CQFB and REQUIMTE, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Monte de Caparica, Portugal.
J Org Chem. 2005 Mar 18;70(6):2120-30. doi: 10.1021/jo048029z.
[reaction: see text] Opposite enantiomers exhibit different NMR properties in the presence of an external common chiral element, and a chiral molecule exhibits different NMR properties in the presence of external enantiomeric chiral elements. Automatic prediction of such differences, and comparison with experimental values, leads to the assignment of the absolute configuration. Here two cases are reported, one using a dataset of 80 chiral secondary alcohols esterified with (R)-MTPA and the corresponding (1)H NMR chemical shifts and the other with 94 (13)C NMR chemical shifts of chiral secondary alcohols in two enantiomeric chiral solvents. For the first application, counterpropagation neural networks were trained to predict the sign of the difference between chemical shifts of opposite stereoisomers. The neural networks were trained to process the chirality code of the alcohol as the input, and to give the NMR property as the output. In the second application, similar neural networks were employed, but the property to predict was the difference of chemical shifts in the two enantiomeric solvents. For independent test sets of 20 objects, 100% correct predictions were obtained in both applications concerning the sign of the chemical shifts differences. Additionally, with the second dataset, the difference of chemical shifts in the two enantiomeric solvents was quantitatively predicted, yielding r(2) 0.936 for the test set between the predicted and experimental values.
[反应：见正文] 对映体在存在外部共同手性元素时表现出不同的核磁共振（NMR）性质，而手性分子在存在外部对映体手性元素时也表现出不同的NMR性质。自动预测此类差异并与实验值进行比较，可实现绝对构型的确定。本文报道了两种情况，一种使用了80种用（R）-MTPA酯化的手性仲醇数据集以及相应的（1）H NMR化学位移，另一种使用了两种对映体手性溶剂中手性仲醇的94个（13）C NMR化学位移。对于第一个应用，训练反向传播神经网络来预测相反立体异构体化学位移之间差异的符号。神经网络被训练以醇的手性编码作为输入，并给出NMR性质作为输出。在第二个应用中，采用了类似的神经网络，但要预测的性质是两种对映体溶剂中化学位移的差异。对于20个对象的独立测试集，在两个应用中关于化学位移差异的符号均获得了100%正确的预测。此外，对于第二个数据集，对两种对映体溶剂中化学位移的差异进行了定量预测，测试集的预测值与实验值之间的r(2)为0.936。
