A set of NF-κB-inducing kinase (NIK) inhibitors was used to develop a molecular docking-based QSAR model by using nonlinear regression method. The accuracy of the QSAR model was remarkably improved by integrating the docking scores and key interaction profiles. Two indole-aminopyrimidine derivatives 32a and 32b predicted as NIK inhibitors were synthesized and biologically evaluated. The significant correlationship between experimental data and MD-SVR model-predicted results were observed. The binding mode of 32a and 32b with NIK were further investigated by dynamic simulations. Compound 32b was proposed as a promising lead for the findings of highly potent inhibitors.

通过使用非线性回归方法，使用一组NF-κB诱导激酶（NIK）抑制剂来建立基于分子对接的QSAR模型。通过整合对接得分和关键交互配置文件，QSAR模型的准确性得到了显着提高。合成了两种被预测为NIK抑制剂的吲哚-氨基嘧啶衍生物32a和32b，并对其进行了生物学评估。观察到实验数据和MD-SVR模型预测的结果之间的显着相关性。通过动态模拟进一步研究了32a和32b与NIK的结合模式。化合物32b被认为是发现高效抑制剂的有前途的先导。