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The efficiency of ligand-receptor interaction information alone as new descriptors in QSAR modeling via random forest artificial neural network.
Chemical Biology & Drug Design ( IF 3.2 ) Pub Date : 2020-04-07 , DOI: 10.1111/cbdd.13690 Zeinab Mozafari 1 , Mansour Arab Chamjangali 1 , Mozhgan Beglari 1 , Rahele Doosti 1
Chemical Biology & Drug Design ( IF 3.2 ) Pub Date : 2020-04-07 , DOI: 10.1111/cbdd.13690 Zeinab Mozafari 1 , Mansour Arab Chamjangali 1 , Mozhgan Beglari 1 , Rahele Doosti 1
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A new approach is introduced for the construction of a predictive quantitative structure–activity relationship model in which only ligand–receptor (LR) interaction features are used as relevant descriptors. This approach combines the benefit of the random forest (RF) as a new variable selection method with the intrinsic capability of the artificial neural network (ANN). The interaction information of the ligand–receptor (LR) complex was used as molecular docking descriptors. The most relevant descriptors were selected using the RF technique and used as inputs of ANN. The proposed RF ANN (RF‐LM‐ANN) method was optimized and then evaluated by the prediction of pEC50 for some of the azine derivatives as non‐nucleoside reverse transcriptase inhibitors. RF‐LM‐ANN model under the optimal conditions was evaluated using internal (validation) and external test sets. The determination coefficients of the external test and validation sets were 0.88 and 0.89, respectively. The mean square deviation (MSE) values for the prediction of biological activities in the external test and validation sets were found to be 0.10 and 0.11, respectively. The results obtained demonstrated the good prediction ability and high generalizability of the proposed RF‐LM‐ANN model based on the MMDs alone.
中文翻译：
通过随机森林人工神经网络将配体-受体相互作用信息的效率单独用作QSAR建模中的新描述符。
引入了一种新的方法来构建预测的定量结构-活性关系模型，其中仅将配体-受体（LR）相互作用特征用作相关的描述符。这种方法结合了作为新的变量选择方法的随机森林（RF）的优势和人工神经网络（ANN）的固有能力。配体-受体（LR）配合物的相互作用信息用作分子对接描述符。使用RF技术选择了最相关的描述符，并将其用作ANN的输入。拟议的RF ANN（RF‐LM‐ANN）方法经过优化，然后通过pEC 50的预测进行评估对于某些嗪衍生物作为非核苷逆转录酶抑制剂。使用内部（验证）和外部测试集评估了最佳条件下的RF‐LM‐ANN模型。外部测试和验证集的确定系数分别为0.88和0.89。发现在外部测试和验证集中用于预测生物活性的均方差（MSE）值分别为0.10和0.11。获得的结果证明了仅基于MMD的RF-LM-ANN模型具有良好的预测能力和较高的推广能力。
更新日期：2020-04-07
中文翻译：
通过随机森林人工神经网络将配体-受体相互作用信息的效率单独用作QSAR建模中的新描述符。
引入了一种新的方法来构建预测的定量结构-活性关系模型，其中仅将配体-受体（LR）相互作用特征用作相关的描述符。这种方法结合了作为新的变量选择方法的随机森林（RF）的优势和人工神经网络（ANN）的固有能力。配体-受体（LR）配合物的相互作用信息用作分子对接描述符。使用RF技术选择了最相关的描述符，并将其用作ANN的输入。拟议的RF ANN（RF‐LM‐ANN）方法经过优化，然后通过pEC 50的预测进行评估对于某些嗪衍生物作为非核苷逆转录酶抑制剂。使用内部（验证）和外部测试集评估了最佳条件下的RF‐LM‐ANN模型。外部测试和验证集的确定系数分别为0.88和0.89。发现在外部测试和验证集中用于预测生物活性的均方差（MSE）值分别为0.10和0.11。获得的结果证明了仅基于MMD的RF-LM-ANN模型具有良好的预测能力和较高的推广能力。
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