天然產(chǎn)物尤其是海洋天然產(chǎn)物是目前國內(nèi)外藥物學家研究的重點。一維核磁數(shù)據(jù)（1H
NMR 和13C NMR）在天然產(chǎn)物結(jié)構(gòu)確定中具有重要地位。本文首次將反向傳播(BP)神經(jīng)網(wǎng)絡(luò)理論應(yīng)用于13C NMR 對1H NMR 化學位移值的預測。通過對初始數(shù)據(jù)進行預處理，使其適應(yīng)BP 神經(jīng)網(wǎng)絡(luò)學習的要求，然后建立基于BP 神經(jīng)網(wǎng)絡(luò)的1H NMR 的數(shù)據(jù)預測模型。采用實際數(shù)據(jù)對模型進行驗證。計算結(jié)果表明人工神經(jīng)網(wǎng)絡(luò)的BP 算法可以用于天然產(chǎn)物一維核磁數(shù)據(jù)的預測。
關(guān)鍵詞： 1H NMR 和13C NMR；神經(jīng)網(wǎng)絡(luò)；BP 算法；預測模型
Abreast: 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.
Key words: 1H NMR and 13C NMR; neural network; BP neural network theory; forecasting model