基于启发式方法和径向基函数神经网络的QSRR/QSAR研究
摘要
定量构效关系(Quantitative Structure-Activity/Property Relationship)方法是目前国际上比较活跃的研究领域之一,QSAR/QSPR的研究领域涉及化学、生物、医学、农业以及环境等诸多学科。QSAR/QSPR利用计算出来的化合物的分子结构参数,建立二维(2-D)、三维(3-D)或多维定量模型,来预测未知化合物的各种理化性质、药物生物活性等。
本论文从定量描述分子的结构和性质定量关系的建立及发展入手,总结了QSAR/QSPR方法在化学研究、色谱分析、环境效应评价和药物设计方面的应用。同时,着重研究了如何运用径向基函数神经网络(Radial Basis Function Neural Networks, RBFNN)方法建立高效、稳定的QSAR/QSPR模型。
第一章介绍了定量构效关系的概念、基本原理、方法、发展历程及研究进展。同时,详细介绍了HM和RBFNN方法的基本原理及应用。
第二章对83种食物中真菌的二级代谢物毒枝菌素在LC-MS/MS中的保留行为进行了QSRR研究。运用启发式方法和RBFNN方法分别建立了这些化合物的结构与其色谱保留值之间的线性和非线性模型。两种方法的判定系数(R2)分别为0.7835和0.8932。通过对两种模型的稳定性和预测能力的比较,发现所选描述符与保留值存在一定的非线性关系,非线性方法RBFNN模型的预测能力有很大改善。该方法与其它非线性方法相比具有简单、快捷、准确的优点。
第三章运用启发式方法和RBFNN方法分别建立了描述65个有机污染物的结构与其生物分配胶束色谱保留值之间的线性和非线性QSRR模型。RBFNN模型的训练集、预测集的判定系数(R2)分别为0.9301和0.9046。结果表明,RBFNN模型性能优于HM。
第四章运用启发式和RBFNN方法分别建立了40个XR类肿瘤多药耐药抑制剂的结构与calcein AM分析中的活性的线性和非线性模型。两种模型对测试集进行预测得到的判定系数(R2)分别为0.8827和0.9276。结果表明,RBFNN模型的预测结果更为准确。
Quantitative structure-activity/property Relationship (QSAR/QSPR) methods are the most promising and successful tools to provide rapid and useful meaning for predicting the biological activity or toxicity of organic compounds by using of different statistical methods and various kinds of molecular descriptors. The aim of QSAR is to develop models on a training set of compounds, these models will then allow for the prediction of the biological activity of related chemicals. This kind of study can not only develop a method for the prediction of the property of compounds that have not been synthesized, but also can identify and describe important structural features of molecules that are relevant to variations in molecular properties, thus gain some insight into structural factors affecting molecular properties. Now, QSAR method has been introduced to environment chemistry and medical chemistry. In this dissertation, we mainly discussed radial basis function neural networks (RBFNN) to construct QSAR model.
Chapter 1 of the dissertation included a brief description of the history, principle, realization process and research status of QSAR/QSPR. In this section, we also introduced the method RBFNN and a review of the application of RBFNN in medical and environment chemistry area.
Chapter 2 of this dissertation described Quantitative Structure-Retention Relationships for Mycotoxins and Fungal Metabolites in LC-MS/MS. Quantitative structure-retention relationship (QSRR) models have been successfully developed for the prediction of the retention time of Mycotoxins and Fungal Metabolites in LC-MS /MS. Heuristic method (HM) and RBFNN were utilized to construct the linear and non-linear QSRR models, respectively. The RBFNN model gave a correlation coefficient (R2) of 0.8709 and root-mean-square error (RMSE) of 1.2892 for the test set. This work provided a useful model for the predicting tR of other mycotoxins when experiment data are unknown.
Chapter 3 of dissertation described the prediction of the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 65 organic pollutants by HM and RBFNN. Heuristic method (HM) and RBFNN were utilized to construct the linear and non-linear QSRR models, respectively. The correlation coefficients (R2) of the nonlinear RBFNN model were 0.9301 and 0.9046 for the training and testing sets, respectively. This work provided a useful model for the predicting the log k of other organic compounds when experiment data are unknown. Compared with the results of HM, the RBFNN obtained more accurate prediction.
Chapter 4 of dissertation described QSAR about multidrug resistance modulators. HM and RBFNN methods were proposed to generate QSAR models for a set of tetrahydroisoquinoline-ethyl-phenylamine substructure to predict their biological activity in the calcein AM assay. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. Compared with the results of HM, the RBFNN obtained more accurate prediction. The correlation coefficients (R2) of the nonlinear RBFNN model were 0.9101 and 0.9276 for the training and testing sets, respectively. This paper proposed an effective method to design new MDR modulators based on QSAR.
本论文从定量描述分子的结构和性质定量关系的建立及发展入手,总结了QSAR/QSPR方法在化学研究、色谱分析、环境效应评价和药物设计方面的应用。同时,着重研究了如何运用径向基函数神经网络(Radial Basis Function Neural Networks, RBFNN)方法建立高效、稳定的QSAR/QSPR模型。
第一章介绍了定量构效关系的概念、基本原理、方法、发展历程及研究进展。同时,详细介绍了HM和RBFNN方法的基本原理及应用。
第二章对83种食物中真菌的二级代谢物毒枝菌素在LC-MS/MS中的保留行为进行了QSRR研究。运用启发式方法和RBFNN方法分别建立了这些化合物的结构与其色谱保留值之间的线性和非线性模型。两种方法的判定系数(R2)分别为0.7835和0.8932。通过对两种模型的稳定性和预测能力的比较,发现所选描述符与保留值存在一定的非线性关系,非线性方法RBFNN模型的预测能力有很大改善。该方法与其它非线性方法相比具有简单、快捷、准确的优点。
第三章运用启发式方法和RBFNN方法分别建立了描述65个有机污染物的结构与其生物分配胶束色谱保留值之间的线性和非线性QSRR模型。RBFNN模型的训练集、预测集的判定系数(R2)分别为0.9301和0.9046。结果表明,RBFNN模型性能优于HM。
第四章运用启发式和RBFNN方法分别建立了40个XR类肿瘤多药耐药抑制剂的结构与calcein AM分析中的活性的线性和非线性模型。两种模型对测试集进行预测得到的判定系数(R2)分别为0.8827和0.9276。结果表明,RBFNN模型的预测结果更为准确。
Quantitative structure-activity/property Relationship (QSAR/QSPR) methods are the most promising and successful tools to provide rapid and useful meaning for predicting the biological activity or toxicity of organic compounds by using of different statistical methods and various kinds of molecular descriptors. The aim of QSAR is to develop models on a training set of compounds, these models will then allow for the prediction of the biological activity of related chemicals. This kind of study can not only develop a method for the prediction of the property of compounds that have not been synthesized, but also can identify and describe important structural features of molecules that are relevant to variations in molecular properties, thus gain some insight into structural factors affecting molecular properties. Now, QSAR method has been introduced to environment chemistry and medical chemistry. In this dissertation, we mainly discussed radial basis function neural networks (RBFNN) to construct QSAR model.
Chapter 1 of the dissertation included a brief description of the history, principle, realization process and research status of QSAR/QSPR. In this section, we also introduced the method RBFNN and a review of the application of RBFNN in medical and environment chemistry area.
Chapter 2 of this dissertation described Quantitative Structure-Retention Relationships for Mycotoxins and Fungal Metabolites in LC-MS/MS. Quantitative structure-retention relationship (QSRR) models have been successfully developed for the prediction of the retention time of Mycotoxins and Fungal Metabolites in LC-MS /MS. Heuristic method (HM) and RBFNN were utilized to construct the linear and non-linear QSRR models, respectively. The RBFNN model gave a correlation coefficient (R2) of 0.8709 and root-mean-square error (RMSE) of 1.2892 for the test set. This work provided a useful model for the predicting tR of other mycotoxins when experiment data are unknown.
Chapter 3 of dissertation described the prediction of the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 65 organic pollutants by HM and RBFNN. Heuristic method (HM) and RBFNN were utilized to construct the linear and non-linear QSRR models, respectively. The correlation coefficients (R2) of the nonlinear RBFNN model were 0.9301 and 0.9046 for the training and testing sets, respectively. This work provided a useful model for the predicting the log k of other organic compounds when experiment data are unknown. Compared with the results of HM, the RBFNN obtained more accurate prediction.
Chapter 4 of dissertation described QSAR about multidrug resistance modulators. HM and RBFNN methods were proposed to generate QSAR models for a set of tetrahydroisoquinoline-ethyl-phenylamine substructure to predict their biological activity in the calcein AM assay. Descriptors calculated from the molecular structures alone were used to represent the characteristics of the compounds. Compared with the results of HM, the RBFNN obtained more accurate prediction. The correlation coefficients (R2) of the nonlinear RBFNN model were 0.9101 and 0.9276 for the training and testing sets, respectively. This paper proposed an effective method to design new MDR modulators based on QSAR.
引文
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