Zanardi María M, Sarotti Ariel M
Instituto de Química Rosario (CONICET), Facultad de Ciencias Bioquímicas y Farmacéuticas, Universidad Nacional de Rosario , Suipacha 531, Rosario 2000, Argentina.
Facultad de Química e Ingeniería "Fray Rogelio Bacón", Pontificia Universidad Católica Argentina , Av. Pellegrini 3314, Rosario 2000, Argentina.
J Org Chem. 2015 Oct 2;80(19):9371-8. doi: 10.1021/acs.joc.5b01663. Epub 2015 Sep 20.
The structural validation problem using quantum chemistry approaches (confirm or reject a candidate structure) has been tackled with artificial neural network (ANN) mediated multidimensional pattern recognition from experimental and calculated 2D C-H COSY. In order to identify subtle errors (such as regio- or stereochemical), more than 400 ANNs have been built and trained, and the most efficient in terms of classification ability were successfully validated in challenging real examples of natural product misassignments.
利用量子化学方法解决结构验证问题（确认或否定候选结构），已通过人工神经网络（ANN）介导的多维模式识别来处理，该识别基于实验和计算得到的二维C-H化学位移相关谱（COSY）。为了识别细微错误（如区域或立体化学错误），已构建并训练了400多个神经网络，并且在天然产物错误归属的具有挑战性的实际例子中，成功验证了分类能力方面最有效的神经网络。
