Dengue fever is a disease transmitted by infected mosquitoes. This disease spreads in several countries, especially those with a tropical climate. To date, there is no specific drug that can be used to treat dengue. Use of clinically investigated drugs, such as Balapiravir, is still not effective in inhibiting the activity of virus replication. The design of a drug candidate can be performed by using the non-structural protein 3 (NS3) as target. This study aimed to develop QSAR models to predict the inhibitory activity class of NS3 inhibitors. The classification was performed by using feature importance analysis for selecting the descriptors and three ensemble methods, i.e. random forest (RF), adaptive boosting (AdaBoost), and extremely randomized trees (ERT), for model design and prediction. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that model 9, developed from ERT produced the best performance with values of accuracy and AUC equal to 0.73 and 0.82, respectively. Use of y-scrambling method allowed us to confirm that the model was not related to the chance correlation.

登革热是由被感染的蚊子传播的疾病。这种疾病在几个国家蔓延，尤其是热带气候国家。迄今为止，尚无可用于治疗登革热的特定药物。使用经过临床研究的药物（如Balapiravir）仍无法有效抑制病毒复制的活性。候选药物的设计可以通过使用非结构蛋白3（NS3）作为靶标来进行。这项研究旨在开发QSAR模型来预测NS3抑制剂的抑制活性类别。通过使用特征重要性分析来选择描述符，并使用三种集成方法（即随机森林（RF），自适应增强（AdaBoost）和极随机树（ERT））来进行分类，以进行模型设计和预测。进行超参数调整以改善模型的性能。根据结果​​，我们发现从ERT开发的模型9产生了最佳性能，其准确度和AUC值分别等于0.73和0.82。y加扰方法的使用使我们可以确认该模型与机会相关性无关。