中文摘要：

本研究选择了5个TLR7激动剂作为训练集,使用Discovery Studio软件包构建了TLR7激动剂的药效团模型。最终获得的最优药效团模型Hypo2由一个氢键受体、一个氢键给体和两个疏水中心组成,对训练集和测试集具有较好的预测能力。此外,将Hypo2作为提问结构搜索由79个不同活性的TLR7激动剂（0.2–5000nM）组成的化合物库,该模型能有效将数据库中高活性的TLR7激动剂识别为目标化合物。分子对接研究进一步验证了该药效团模型的合理性。本研究获得的TLR7激动剂药效团模型有助于发现新型TLR7激动剂。

英文摘要：

Toll-like receptor 7 （TLR7）, the best known TLRs, has been demonstrated to be useful in fighting against infectious disease. In our study, three-dimensional （3D） pharmacophore models were constructed from a set of 5 TLR7 agonists. Among the 10 common-featured models generated by program Discovery Studio/HipHop, a hypothesis （Hypo2） including one hydrogen-bond donor （D）, one hydrogen-bond acceptor （A）, and two hydrophobic （H） features was considered to be important in evaluating the ligands with TLR7 agonistic activity. The obtained pharmacophore model was further validated using a set of test molecules and the Catalyst TLR7-agonist-subset database. Hypo2 has been shown to identify a range of highly potent TLR7 agonists. Finally, the obtained pharmacophore was further validated using docking studies. Taken together, this model can be utilized as a guide for future studies to design the structurally novel TLR7 agonists.