20世纪70年代,计算机辅助药物设计(Computer-Aided Drug Design,CADD)正式发展为一门新兴技术.分子对接技术(Molecular Docking)作为计算机辅助药物设计的一种主要方法,目前已被广泛应用于新药研发的多个环节.本文不仅介绍了分子对接的...20世纪70年代,计算机辅助药物设计(Computer-Aided Drug Design,CADD)正式发展为一门新兴技术.分子对接技术(Molecular Docking)作为计算机辅助药物设计的一种主要方法,目前已被广泛应用于新药研发的多个环节.本文不仅介绍了分子对接的基本原理、分子对接常用方法和常用软件,还着重介绍了分子对接在新药研发中的具体应用,包括药物发现阶段的早期虚拟筛选、药物作用靶点发现、药物潜在作用机制研究以及药物代谢位点的预测.此外,还对分子对接技术在新药研发领域的应用前景进行展望.展开更多
AIM: To develop an artificial neural network (ANN) model for predicting the resistance index (RI) of taxoids. METHODS: A dataset of 63 experimental data points were compiled from literatures and subdivided into traini...AIM: To develop an artificial neural network (ANN) model for predicting the resistance index (RI) of taxoids. METHODS: A dataset of 63 experimental data points were compiled from literatures and subdivided into training and external test sets randomly. Electrotopological state (E-state) indices were calculated to characterize molecular structure, together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network (BPNN) technique was used to build the models. Five-fold cross validation was performed and five models with different compounds composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. RESULTS: The final model was proved to be good with the cross validation Qcv2 0.62, external testing R2 0.84 and the slope of the regression line through the origin for testing set is 0.9933. CONCLUSION: The QSAR model can predict the RI to a relative nicety, which will aid in the development of new anti-MDR taxoids.展开更多
文摘20世纪70年代,计算机辅助药物设计(Computer-Aided Drug Design,CADD)正式发展为一门新兴技术.分子对接技术(Molecular Docking)作为计算机辅助药物设计的一种主要方法,目前已被广泛应用于新药研发的多个环节.本文不仅介绍了分子对接的基本原理、分子对接常用方法和常用软件,还着重介绍了分子对接在新药研发中的具体应用,包括药物发现阶段的早期虚拟筛选、药物作用靶点发现、药物潜在作用机制研究以及药物代谢位点的预测.此外,还对分子对接技术在新药研发领域的应用前景进行展望.
文摘AIM: To develop an artificial neural network (ANN) model for predicting the resistance index (RI) of taxoids. METHODS: A dataset of 63 experimental data points were compiled from literatures and subdivided into training and external test sets randomly. Electrotopological state (E-state) indices were calculated to characterize molecular structure, together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network (BPNN) technique was used to build the models. Five-fold cross validation was performed and five models with different compounds composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models. RESULTS: The final model was proved to be good with the cross validation Qcv2 0.62, external testing R2 0.84 and the slope of the regression line through the origin for testing set is 0.9933. CONCLUSION: The QSAR model can predict the RI to a relative nicety, which will aid in the development of new anti-MDR taxoids.
