首页> 外文期刊>Toxicology Letters: An International Journal Providing a Forum for Original and Pertinent Contributions in Toxicology Research >Prediction of retention times for a large set of pesticides or toxicants based on support vector machine and the heuristic method.
【24h】

Prediction of retention times for a large set of pesticides or toxicants based on support vector machine and the heuristic method.

机译:基于支持向量机和启发式方法的大量农药或毒物的保留时间预测。

获取原文
获取原文并翻译 | 示例
           

摘要

Quantitative structure-retention relationship (QSRR) studies were performed for predicting the retention times (RTs) of 110 kinds of pesticides or toxicants. Chemical descriptors were calculated from the molecular structure of the compounds alone. The QSRR models were built using the heuristic method (HM) and support vector machine (SVM), respectively. The obtained linear model of HM had a square of a correlation coefficient: R(2)=0.913, F=116.70 with a root mean square error (RMS) error of 0.0387 for the training set, while R(2)=0.907, F=195.49, and RMS=0.0408 for the test set. The non-linear model by SVM gave better results: for the training set R(2)=0.966, F=2420.5, RMS=0.0231 and for the test set R(2)=0.944, F=339.7, RMS=0.0313. The prediction results are in good agreement with the experimental values. And the proposed model could identify and provide some insight into what structural features are related to retention time of these compounds.
机译:进行了定量结构保留关系(QSRR)研究,以预测110种农药或有毒物质的保留时间(RTs)。仅从化合物的分子结构计算化学描述符。 QSRR模型分别使用启发式方法(HM)和支持向量机(SVM)构建。获得的HM线性模型具有一个相关系数的平方:R(2)= 0.913,F = 116.70,训练集的均方根误差(RMS)误差为0.0387,而R(2)= 0.907,F = 195.49,测试集的RMS = 0.0408。 SVM的非线性模型给出了更好的结果:对于训练集R(2)= 0.966,F = 2420.5,RMS = 0.0231,对于测试集R(2)= 0.944,F = 339.7,RMS = 0.0313。预测结果与实验值吻合良好。所提出的模型可以识别哪些化合物的结构特征与保留时间相关并提供一些见识。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号