数量结构-活动关系
随机森林
人工神经网络
概化理论
人工智能
分子描述符
计算机科学
均方误差
生物系统
机器学习
数学
统计
生物
作者
Zeinab Mozafari,Mansour Arab Chamjangali,Mozhgan Beglari,Rahele Doosti
摘要
Abstract 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 50 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.
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