QSAR Study On Activity Of Benzodiazepine And Purine-based Nucleosides

Quantitative structure-activity relationship (QSAR) methods, are the most promising and successful tools to provide rapid and useful meanings for predicting the biological activity or toxicities of organic compounds by use of different statistical methods and various kinds of molecular descriptors. The goal 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 biology, chemistry and environment science. In this dissertation, we mainly discussed a novel machine learning method, support vector machine (SVM) to construct QSAR model.Chapter 1 of the dissertation included a brief description of the QSAR history, principle, realization process and research status. In this section, we also introduced the support vector machine method and a review of the application of SVM in biology, chemistry and environment area.