State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, West China Medical School, Sichuan University, No. 1, Keyuan Rd. 4, Gaopeng Avenue, High Tech Park, Chengdu, Sichuan 610041, China.
J Biomol Struct Dyn. 2011 Aug;29(1):165-79. doi: 10.1080/07391102.2011.10507381.
IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.
IKK2（IκB 激酶 2）抑制剂已被确定为治疗各种免疫/炎症性疾病和癌症的潜在药物候选物。到目前为止，已经公开报道了超过一百种针对 IKK2 的小分子抑制剂。在这项研究中，进行了药效团建模，以阐明已知 IKK2 抑制剂的关键结构-活性关系。所建立的药效团假设之一，即 Hypo8，对外部测试数据集具有最佳的预测能力，被建议作为虚拟筛选的模板。Hypo8 和混合方法（Hypo81docking）在虚拟筛选中的性能评估表明，使用混合虚拟筛选大大提高了命中率和富集因子。因此，采用混合虚拟筛选方法对包括 Specs、NCI、Maybridge 和中国天然产物数据库（CNPD）在内的几种商业上可用的化学数据库进行了筛选，以寻找新型有效的 IKK2 抑制剂。随后，对命中化合物进行了 Lipinski 五规则的筛选。最后，选择了一些最终的命中化合物，并建议进行进一步的实验研究。
