Janus kinase 1 (JAK1) is protein kinase involved in autoimmune diseases (AIDs). JAK1 inhibitors have shown promising results in treating AIDs. JAK1 inhibitors are known to exhibit regions of SAR discontinuity or activity cliffs (ACs). ACs represent fundamental challenge to successful QSAR/pharmacophore modeling because QSAR modeling rely on the basic premise that activity is a smooth continuous function of structure. We propose that ACs exist because active ACs members exhibit subtle, albeit critical, enthalpic features absent from their inactive twins. In this context we compared the performances of two computational modeling workflows in extracting valid pharmacophores from 151 diverse JAK1 inhibitors that include ACs: QSAR-guided pharmacophore selection versus docking-based comparative intermolecular contacts analysis (db-CICA). The two methods were judged based on the receiver operating characteristic (ROC) curves of their corresponding pharmacophore models and their abilities to distinguish active members among established JAK1 ACs. db-CICA modeling significantly outperformed ligand-based pharmacophore modeling. The resulting optimal db-CICA pharmacophore was used as virtual search query to scan the National Cancer Institute (NCI) database for novel JAK1 inhibitory leads. The most active hit showed IC₅₀ of 1.04 μM. This study proposes the use of db-CICA modeling as means to extract valid pharmacophores from SAR data infested with ACs.

Janus激酶1（JAK1）是涉及自身免疫性疾病（AID）的蛋白激酶。JAK1抑制剂在治疗AID中显示出令人鼓舞的结果。已知JAK1抑制剂具有SAR不连续或活动悬崖（AC）区域。AC代表了成功的QSAR /药效团建模的根本挑战，因为QSAR建模依赖于基本前提，即活动是结构的平稳连续功能。我们提出AC的存在是因为活跃的AC成员表现出微弱的，尽管很关键的，焓的特征，而它们的不活跃双胞胎却没有。在这种情况下，我们比较了两种计算建模工作流程在从151种包含AC的各种JAK1抑制剂中提取有效药效团的性能：QSAR指导的药效团选择与基于对接的比较分子间接触分析（db-CICA）。根据相应药效团模型的受体工作特征（ROC）曲线以及已建立的JAK1 AC之间区分活性成员的能力来判断这两种方法。db-CICA建模明显优于基于配体的药效团建模。生成的最佳db-CICA药效基团用作虚拟搜索查询，以扫描美国国家癌症研究所（NCI）数据库中的新型JAK1抑制性前导。最活跃的结果显示IC ₅₀为1.04μM。这项研究建议使用db-CICA模型作为从受AC侵扰的SAR数据中提取有效药效团的手段。