Despite the well-known adverse health effects associated with tobacco use, addiction to nicotine found in tobacco products causes difficulty in quitting among users. Nicotinic acetylcholine receptors (nAChRs) are the physiological targets of nicotine and facilitate addiction to tobacco products. The nAChR-α7 subtype plays an important role in addiction; therefore, predicting the binding activity of tobacco constituents to nAChR-α7 is an important component for assessing addictive potential of tobacco constituents. We developed an α7 binding activity prediction model based on a large training data set of 843 chemicals with human α7 binding activity data extracted from PubChem and ChEMBL. The model was tested using 1215 chemicals with rat α7 binding activity data from the same databases. Based on the competitive docking results, the docking scores were partitioned to the key residues that play important roles in the receptor-ligand binding. A decision forest was used to train the human α7 binding activity prediction model based on the partition of docking scores. Five-fold cross validations were conducted to estimate the performance of the decision forest models. The developed model was used to predict the potential human α7 binding activity for 5275 tobacco constituents. The human α7 binding activity data for 84 of the 5275 tobacco constituents were experimentally measured to confirm and empirically validate the prediction results. The prediction accuracy, sensitivity, and specificity were 64.3, 40.0, and 81.6%, respectively. The developed prediction model of human α7 may be a useful tool for high-throughput screening of potential addictive tobacco constituents.

中文翻译：

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烟碱乙酰胆碱受体nAChRα7结合活性预测模型的开发。

尽管众所周知与使用烟草有关的不利健康影响，但烟草产品中发现的尼古丁成瘾会导致戒烟困难。烟碱乙酰胆碱受体（nAChRs）是尼古丁的生理靶标，可促进烟草制品成瘾。nAChR-α7亚型在成瘾中起重要作用。因此，预测烟草成分与nAChR-α7的结合活性是评估烟草成分成瘾潜力的重要组成部分。我们基于843种化学物质的大型训练数据集开发了一个α7结合活性预测模型，并从PubChem和ChEMBL中提取了人类α7结合活性数据。使用来自相同数据库的具有大鼠α7结合活性数据的1215种化学物质测试了该模型。根据竞争对接结果，对接分数被划分为在受体-配体结合中起重要作用的关键残基。基于对接得分的划分，决策森林用于训练人类α7结合活性预测模型。进行了五次交叉验证，以评估决策森林模型的性能。所开发的模型用于预测5275种烟草成分的潜在人类α7结合活性。对5275种烟草成分中84种的人α7结合活性数据进行了实验测量，以确认并凭经验验证预测结果。预测准确性，敏感性和特异性分别为64.3、40.0和81.6％。已开发的人类α7预测模型可能是高通量筛选潜在成瘾性烟草成分的有用工具。