ABCG2 is the principal ABC transporter involved in the multidrug resistance of breast cancer. Looking at the current demand in the development of ABCG2 inhibitors for the treatment of multidrug-resistant cancer, we have explored structural requirements of phenyltetrazole derivatives for ABCG2 inhibition by combining classical QSAR, Bayesian classification modelling and molecular docking studies. For classical QSAR, structural descriptors were calculated from the free software tool PaDEL-descriptor. Stepwise multiple linear regression (SMLR) was used for model generation. A statistically significant model was generated and validated with different parameters (For training set: r = 0.825; Q² = 0.570 and for test set: r = 0.894, r²_pred = 0.783). The predicted model was found to satisfy the Golbraikh and Trospha criteria for model acceptability. Bayesian classification modelling was also performed (ROC scores were 0.722 and 0.767 for the training and test sets, respectively). Finally, the binding interactions of phenyltetrazole type inhibitor with the ABCG2 receptor were mapped with the help of molecular docking study. The result of the docking analysis is aligned with the classical QSAR and Bayesian classification studies. The combined modelling study will guide the medicinal chemists to act faster in the drug discovery of ABCG2 inhibitors for the management of resistant breast cancer.

ABCG2是涉及乳腺癌多药耐药性的主要ABC转运蛋白。鉴于目前开发用于治疗多药耐药性癌症的ABCG2抑制剂的需求，我们结合了经典QSAR，贝叶斯分类模型和分子对接研究，探索了苯基四唑衍生物对ABCG2抑制的结构要求。对于经典的QSAR，结构描述符是通过免费软件工具PaDEL-descriptor计算的。使用逐步多元线性回归（SMLR）进行模型生成。生成了具有统计意义的模型，并使用不同的参数进行了验证（对于训练集：r = 0.825；Q ²  = 0.570，对于测试集：r = 0.894，r ²_pred  = 0.783）。发现预测的模型满足模型可接受性的Golbraikh和Trospha标准。还进行了贝叶斯分类建模（训练集和测试集的ROC分数分别为0.722和0.767）。最后，借助分子对接研究，绘制了苯基四唑类抑制剂与ABCG2受体的结合相互作用图。对接分析的结果与经典QSAR和贝叶斯分类研究一致。组合模型研究将指导药用化学家在ABCG2抑制剂的药物发现中更快地采取行动，以治疗耐药性乳腺癌。