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乌头碱类化合物属于二萜类生物碱,存在于乌头属欧乌头、川乌、北草乌和华乌头等多种毛茛科植物中,该类化合物既是其活性成分,也是其毒性成分。利用化合物定量结构-毒性效应关系(QSTR)方法研究了14个乌头碱类化合物的各种量化参数对其毒性的影响,并建立了毒性预测模型。由于该类化合物是从中药中提取分离的生物碱,样本数量相对较少,本文采用了偏最小二乘回归方法(PLS)进行降维,进行4个成分的提取及建模。毒性预测模型为:Log(toxi)=0.1593*Mass+0.2908~*LogP+1.5475~*SAA-0.5222~*SAG-0.6104~*Volume+0.3112~*Ref+0.1784~*Polar+0.1785~*BE+0.1634~*HF-0.1387~*Dipole+0.1412,结果表明该模型具有较好的毒性预测能力。
Aconitum compounds are diterpenoid alkaloids, which are present in various Ranunculaceae plants of the genus Aconitum, Aconitum, Aconitum and Aconite, which are both active components and toxic components . The quantitative structure-toxicity relationship (QSTR) method was used to study the effects of various quantitative parameters of 14 aconitines on their toxicity and to establish a toxicity prediction model. Because these compounds are alkaloids extracted from traditional Chinese medicine, the number of samples is relatively small. In this paper, partial least-squares regression (PLS) is used to reduce the dimension, and four components are extracted and modeled. The toxicity prediction model was as follows: Log (toxi) = 0.1593 * Mass + 0.2908 ~ * LogP + 1.5475 ~ * SAA- 0.5222 ~ * SAG- 0.6104 ~ * Volume + 0.3112 ~ * Ref + 0.1784 ~ * Polar + 0.1785 ~ * BE + 0.1634 ~ * HF-0.1387 ~ * Dipole + 0.1412, the results show that the model has good ability of predicting toxicity.