用多元线性回归和人工神经网络方法预测2 -苯氧苄胺衍生物的IC50值

IF 1 Q4 CHEMISTRY, MULTIDISCIPLINARY
Fariba Masoomi Sefiddashti, Hedayat Haddadi, S. Asadpour, Shima Ghanavati Nasab
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引用次数: 1

摘要

在这项研究中,从变量库中选择6个分子描述符,使用逐步回归建立了一系列2-苄基苯甲酰胺衍生物作为SMS2抑制剂减少动脉粥样硬化的QSAR模型。采用简单多元线性回归(MLR)和非线性人工神经网络(ANN)方法对化合物的生物活性进行建模。共建立了34个2-苄基氧苄酰胺衍生物的模型。采用主成分分析法将化合物分为两组,分别为两个训练系列和测试。以27个组合作为训练集构建模型,然后用剩下的7个组合对模型的有效性和预测能力进行评价。虽然MLR提供了一个可接受的预测模型,但基于人工神经网络的模型显著提高了预测能力。在人工神经网络模型中,预测集的平均相对误差(RE%)小于1%,平方相关系数(R2)为0.9912。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of IC50 Values of 2−benzyloxybenzamide Derivatives using Multiple Linear Regression and Artificial Neural Network Methods
In this study, six molecular descriptors were selected from a pool of variables using stepwise regression to built a QSAR model for a series of 2-benzyloxy benzamide derivatives as an SMS2 inhibitor to reduce atherosclerosis. Simple multiple linear regression (MLR) and a nonlinear method, artificial neural network (ANN), were used to modeling the bioactivities of the compounds. Modeling was carried out in total with 34 compounds of 2-benzyl oxybenzamide derivatives. PCA was used to divide the compounds into two groups of two training series and tests. The model was constructed with 27 combinations as training set, then the validity and predictive ability of the model were evaluated with the remaining 7 combinations. While the MLR provides an acceptable model for predictions, the ANN-based model significantly improves the predictive ability. In ANN model the average relative error (RE%) of prediction set is lower than 1% and square correlation coefficient (R2) is 0.9912.
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来源期刊
Iranian journal of mathematical chemistry
Iranian journal of mathematical chemistry CHEMISTRY, MULTIDISCIPLINARY-
CiteScore
2.10
自引率
7.70%
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0
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