Janus激酶1（JAK1）通过JAK/STAT信号通路，在大多数细胞因子介导的炎症、自身免疫反应和多种癌症中发挥关键作用。因此，抑制JAK1成为治疗多种疾病的一种吸引人的策略。最近，高效机器学习技术在虚拟筛选中越来越广泛地应用于新型激酶抑制剂的开发。我们的研究旨在基于机器学习（ML）和药效模型开发一种新的分层虚拟筛选方法，以识别潜在的JAK1抑制剂。首先通过构建一个高质量的数据集，其中包含3834个JAK1抑制剂和12,230个诱骗化合物，接着基于三种分子描述符和六个ML算法的组合建立了一系列的分类模型。为了进一步筛选潜在化合物，我们根据Hiphop和受体配体算法构建了几个药效模型。然后，我们使用分子对接来筛选已识别的化合物。最后，通过分子动力学（MD）模拟和体外酶活性测试评估了识别化合物的结合稳定性和酶抑制活性。最优性能的ML模型DNN-ECFP4和两个药效模型Hiphop3和6TPF 08被用于筛选ZINC数据库。总共筛选出了13个潜在活性化合物，MD结果表明上述所有的分子在动态条件下都能与JAK1稳定结合。在入选化合物中，其中四个可购得化合物表现出显著的激酶抑制活性，其中Z-10活性最强（IC50 = 194.9 nM）。本研究提供了一种高效准确的综合模型。这些筛选出的化合物是进一步开发新型JAK1抑制剂的有 promising 潜在候选物。© 2023. BioMed Central Ltd., Springer Nature 的一部分。

Janus kinase 1 (JAK1) plays a critical role in most cytokine-mediated inflammatory, autoimmune responses and various cancers via the JAK/STAT signaling pathway. Inhibition of JAK1 is therefore an attractive therapeutic strategy for several diseases. Recently, high-performance machine learning techniques have been increasingly applied in virtual screening to develop new kinase inhibitors. Our study aimed to develop a novel layered virtual screening method based on machine learning (ML) and pharmacophore models to identify the potential JAK1 inhibitors.Firstly, we constructed a high-quality dataset comprising 3834 JAK1 inhibitors and 12,230 decoys, followed by establishing a series of classification models based on a combination of three molecular descriptors and six ML algorithms. To further screen potential compounds, we constructed several pharmacophore models based on Hiphop and receptor-ligand algorithms. We then used molecular docking to filter the recognized compounds. Finally, the binding stability and enzyme inhibition activity of the identified compounds were assessed by molecular dynamics (MD) simulations and in vitro enzyme activity tests.The best performance ML model DNN-ECFP4 and two pharmacophore models Hiphop3 and 6TPF 08 were utilized to screen the ZINC database. A total of 13 potentially active compounds were screened and the MD results demonstrated that all of the above molecules could bind with JAK1 stably in dynamic conditions. Among the shortlisted compounds, the four purchasable compounds demonstrated significant kinase inhibition activity, with Z-10 being the most active (IC50 = 194.9 nM).The current study provides an efficient and accurate integrated model. The hit compounds were promising candidates for the further development of novel JAK1 inhibitors.© 2023. BioMed Central Ltd., part of Springer Nature.