中文摘要：

为深入认识含氟农药生物活性与其结构之间的关系，建立了理想的QSAR模型，从化合物油水分配系数等7个分子结构描述符出发，基于支持向量回归（SVR）和MSE最小原则，经自动寻找最优核函数和非线性筛选描述符，构建了多个K-最近邻（KNN）预测子模型．再经非线性筛选获得保留子模型，以保留子模型实施组合预测（Multi—KNN—SVR）．33种含氟化合物对5种不同病害生物活性的留一法组合预测结果表明，采用非线性筛选描述符和KNN子模型能有效地提高预测精度，基于多个KNN子模型的非线性组合能进一步提高预测性能．Multi—KNN—SVR组合预测在QSAR以及其它相关预测研究中具有广泛应用前景．

英文摘要：

To further understand the quantitative structure-activity relationship （QSAR） of fluorine-containing pesticide and improve the prediction precision of QSAR models, a novel nonlinear combinatorial forecast method named Multi-KNN-SVR, multi-K-nearest neighbor based on support vector regression, was proposed. The novel method includes the following key steps： firstly, seeking the best kernel automatically based on the minimum mean square error （MSE） ; secondly, screening descriptors nonlinearly by F-test; finally, carrying out the combinatorial forecast with multiple KNN sub-models. Muhi-KNN-SVR was applied to the QSAR for the antibacterial bioactivities of 33 fluorine-containing pesticides against 5 different plant diseases. The results of leave-one-out test show that screening descriptors and sub-models were essential, and the combinatorial forecast after screening sub-models could get a better precision than single KNN model. The predicte results also indicated that Muhi-KNN-SVR had the advantages of high prediction precision （ MSE = 0. 005-0. 015, MAPE- 2. 136-3. 164） , high stability, strong generalization ability, structural risk minimization, non-linear characteristics and avoiding the over-fit in all reference models. Muhi-KNN-SVR, therefore, can be widely used in QSAR and other related fields.