天然产物尤其是海洋天然产物是目前国内外药物学家研究的重点。一维核磁数据（1HNMR 和13C NMR）在天然产物结构确定中具有重要地位。本文首次将反向传播(BP)神经网络理论应用于13C NMR 对1H NMR 化学位移值的预测。通过对初始数据进行预处理，使其适应BP 神经网络学习的要求，然后建立基于BP 神经网络的1H NMR 的数据预测模型。采用实际数据对模型进行验证。计算结果表明人工神经网络的BP 算法可以用于天然产物一维核磁数据的预测。关键词： 1H NMR 和13C NMR；神经网络；BP 算法；预测模型Abreast: Natural products, especially marine natural products, are paid more attention bypharmacolist. The data of 1H NMR and 13C NMR are the most important to determination ofstructure s of natural compounds. The Back Propagation (BP) neural network theory is first used to predict the relation between the data of 1H NMR and 13C NMR. During the investigation, the original data are preprocessed to meet the requirements of the study in BP neural work, and a forecasting model based on BP neural network is established. The proposed model is then verified by used actual data. Through the results from the model, the BP neural network can be used to forecasting the data of 1D NMR.Key words: 1H NMR and 13C NMR; neural network; BP neural network theory; forecasting model
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