Abstract A combination of least absolute shrinkage and selection operator (LASSO) with Bayesian Regularization feed-forward artificial neural network (LASSO-BR-ANN) was used as a new approach in the quantitative structure-activity relationship (QSAR) studies. A mixture of the docking derived descriptors with the simple functional group (structural) features was also introduced as a new ensemble of descriptors for accurate QSAR modeling. The performance of introduced approaches was tested with QSAR modeling of the biological activities (pEC50) of 73 azine derivatives as new non-nucleoside reverse transcriptase inhibitors (NNRTIs) for treatment of HIV disease. Molecular docking descriptors (MDDs) were generated from ligand-receptor interactions and functional group features derived using Dragon 5.5 software. The dataset was divided into three sets of training, validation, and test data. LASSO, as a penalized regression method, was applied to the training data set for the selection of the most relevant descriptors among the mixture of the structural and MDDs. LASSO selected descriptors were used as inputs in the construction of the Bayesian Regularization artificial neural network (BR-ANN) model. The results showed that the addition of functional group properties to the MDDs improves the accuracy of the model. Under the optimum conditions, LASSO-BR-ANN was successfully applied for the prediction of PEC50 values for compounds in the external test set with mean square error (MSE) and coefficient of determination (R2) values of 0.07 and 0.88, respectively. Some of the prediction statistical parameters of the model were calculated and all of them were in their acceptable ranges, which confirm the validity of the proposed QSAR model.

中文翻译：

---

最小绝对收缩和选择算子与贝叶斯正则化人工神经网络 (LASSO-BR-ANN) 的组合，用于使用功能组和分子对接混合描述符的 QSAR 研究

摘要 最小绝对收缩和选择算子（LASSO）与贝叶斯正则化前馈人工神经网络（LASSO-BR-ANN）的组合被用作定量构效关系（QSAR）研究的新方法。还引入了对接派生描述符与简单功能组（结构）特征的混合，作为用于精确 QSAR 建模的新描述符集合。引入方法的性能通过 73 种吖嗪衍生物的生物活性 (pEC50) 的 QSAR 模型进行测试，该模型作为治疗 HIV 疾病的新型非核苷逆转录酶抑制剂 (NNRTI)。分子对接描述符 (MDD) 是从配体-受体相互作用和使用 Dragon 5.5 软件得出的功能组特征生成的。数据集分为训练、验证和测试数据三组。作为一种惩罚回归方法，LASSO 被应用于训练数据集，以在结构和 MDD 的混合物中选择最相关的描述符。LASSO 选择的描述符用作构建贝叶斯正则化人工神经网络 (BR-ANN) 模型的输入。结果表明，将官能团属性添加到 MDD 提高了模型的准确性。在最佳条件下，LASSO-BR-ANN 成功地用于预测外部测试集中化合物的 PEC50 值，均方误差 (MSE) 和决定系数 (R2) 值分别为 0.07 和 0.88。