The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors w...The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.展开更多
The nature and origin of a fundamental quantum QSPR （QQSPR） equation are discussed. In principle, as any molecular structure can be associated to quantum mechanical density functions （DF）, a molecular set can be r...The nature and origin of a fundamental quantum QSPR （QQSPR） equation are discussed. In principle, as any molecular structure can be associated to quantum mechanical density functions （DF）, a molecular set can be reconstructed as a quantum multimolecular polyhedron （QMP）, whose vertices are formed by each molecular DF. According to QQSPR theory, complicated kinds of molecular properties, like biological activity or toxicity, of molecular sets can be calculated via the quantum expectation value of an approximate Hermitian operator, which can be evaluated with the geometrical information contained in the attached QMP via quantum similarity matrices. Practical ways of solving the QQSPR problem from the point of view of QMP geometrical structure are provided. Such a development results into a powerful algorithm, which can be implemented within molecular design as an alternative to the current classical QSPR procedures.展开更多
基金Project supported by the Postdoctoral Science Foundation of Central South University,ChinaProject(2015SK20823)supported by Science and Technology Project of Hunan Province,China+2 种基金Project(15A001)supported by Scientific Research Fund of Hunan Provincial Education Department,ChinaProject(CX2015B372)supported by Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject supported by Innovation Experiment Program for University Students of Changsha University of Science and Technology,China
文摘The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.
文摘The nature and origin of a fundamental quantum QSPR （QQSPR） equation are discussed. In principle, as any molecular structure can be associated to quantum mechanical density functions （DF）, a molecular set can be reconstructed as a quantum multimolecular polyhedron （QMP）, whose vertices are formed by each molecular DF. According to QQSPR theory, complicated kinds of molecular properties, like biological activity or toxicity, of molecular sets can be calculated via the quantum expectation value of an approximate Hermitian operator, which can be evaluated with the geometrical information contained in the attached QMP via quantum similarity matrices. Practical ways of solving the QQSPR problem from the point of view of QMP geometrical structure are provided. Such a development results into a powerful algorithm, which can be implemented within molecular design as an alternative to the current classical QSPR procedures.
