基于BP神经网络对NMR的预测模型

Forecasting Model for Data of NMR Based on BP Neural Network

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13CNMR)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13CNMR对1HNMR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1HNMR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。

Natural products, especially marine natural products, are paid more attention by pharmacolist. The data of 1H NMR and 13C NMR are the most important to determination of structure 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.