Mozafari Zeinab, Arab Chamjangali Mansour, Beglari Mozhgan, Doosti Rahele
Department of Chemistry, Shahrood University of Technology, Shahrood, Iran.
Chem Biol Drug Des. 2020 Aug;96(2):812-824. doi: 10.1111/cbdd.13690. Epub 2020 May 5.
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 pEC 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.
本文介绍了一种构建预测性定量构效关系模型的新方法,该模型仅将配体-受体(LR)相互作用特征用作相关描述符。此方法将随机森林(RF)作为一种新的变量选择方法的优势与人工神经网络(ANN)的内在能力相结合。配体-受体(LR)复合物的相互作用信息用作分子对接描述符。使用RF技术选择最相关的描述符,并将其用作ANN的输入。对所提出的RF ANN(RF-LM-ANN)方法进行了优化,然后通过预测一些嗪衍生物作为非核苷类逆转录酶抑制剂的pEC进行评估。在最佳条件下的RF-LM-ANN模型使用内部(验证)和外部测试集进行评估。外部测试集和验证集的决定系数分别为0.88和0.89。发现外部测试集和验证集预测生物活性的均方偏差(MSE)值分别为0.10和0.11。仅基于MMD获得的结果证明了所提出的RF-LM-ANN模型具有良好的预测能力和高度的通用性。