为了解决使用三维定量构效关系(three-dimensional quantitative structure-activity relationship,3D-QSAR)模型预测新化合物生物活性效果不理想的问题,建立了2种新的一致性模型.模型一是由多元线性回归(multiple linear regression,MLR)方法构建的加权一致性模型(weighted consensus modeling,WCM),该模型为每个子模型添加了各自的权重系数.模型二通过计算多个子模型预测值的平均值来构建平均一致性模型(average consensus modeling,ACM).研究结果表明,当交叉验证相关系数0.50.8时不能提高3D-QSAR模型的预测能力.该方法可为提高模型预测能力和设计新型高活性抑制剂提供帮助.%To solve the problem that the three-dimensional quantitative structure-activity relationship (3D-QSAR) model is not ideal when using the model to predict the biological activity of the new compounds, two new consensus models were established to improve the prediction ability of the model. A different weight to each submodule ( named weighted consensus model, WCM) was added to one of the consensus models. In order to construct WCM, multiple linear regression ( MLR) methods were used to calculate different weight coefficients for each submodule. Another consensus model was constructed from the average of the predicted values for each sub-model obtained in the literature ( named average consensus model, ACM) . Results show that the consensus model can improve the prediction ability when 0. 50. 8. This result can help to improve the prediction of the model and the design of new high activity inhibitors.
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