Discovery of a Novel 5-HT_(2A) Inhibitor by Pharmacophore-based Virtual Screening

The serotonin 2A（5-HT2A） receptor has been implicated in several neurological conditions and potent 5-HT2A antagonists have therapeutic effects in the treatment of schizo phrenia and depression.In this study,a potent novel 5-HT2A inhibitor 05245768 with a Ki value of （593.89±34.10） nmol/L was discovered by integrating a set of computational approaches and experiments（protein structure prediction,pharmacophore-based virtual screening,automated molecular docking and pharmacological bioassay）.The 5-HT2A receptor showed a negatively charged bin-ding pocket.The binding mode of compound 05245768 with 5-HT2A was obtained by GOLD docking procedure,which revealed the conserved interaction between protonated nitrogen in compound 05245768 and carboxylate group of D3.32 at the active site of 5-HT2A.

Pharmacophore Based Virtual Screening for Identification of Novel CDK₂Inhibitors

CDK2 is one of the most important members of Cyclin-dependent kinases. It is a critical modulator of various oncogenic signaling pathways, and its activity is vital for loss of proliferative control during oncogenesis. This work has focused on developing a pharmacophore model for CDK2 inhibitors by using a dataset of known inhibitors as a pre-filter throughout the virtual screening and docking process. Consequently, the best pharmacophore model was made of one hydrogen bond acceptor, and two aromatic ring features with a high correlation value of 0.906. The validation findings proved out that the selected model can be used as a filter to screen new molecules like Enamine kinase hinge region directed library against CDK2. As a result, 69 hits were subjected to molecular docking studies. Eventually, three compounds (5909, 701 and 8397) scored good interaction energy values and strong molecular interactions. Hence, they were identified as leads for novel CDK2 inhibitors as anticancer drugs.

整合药效团的分子对接方法研究

通过对计算机药物虚拟筛选技术、Dock评分函数体系和PocketV.2评分方法的分析,研究Dock评分函数体系及其源代码、PocketV.2评分方法及其源代码,提出基于药效团的计算机药物虚拟筛选方法。利用模拟配体与受体真实相互作用的分子间距离及性质的点模式匹配算法,实现了基于药效团的计算机药物虚拟筛选。在超性能计算集群环境下运行,并对该算法进行了测试,结果表明具有较强的可靠性和较高的准确性。

基于药效团模型虚拟筛选活络丸中治疗骨关节炎的COX-2抑制成分

目的采用药效团模型-分子对接-结合自由计算的分层虚拟筛选策略结合酶活性抑制实验,挖掘活络丸中对环氧合酶2(cyclooxygenase-2,COX-2)有抑制作用的成分。方法使用Schrödinger 2020-4软件中PHASE模块进行COX-2小分子抑制剂药效团模型的构建,应用筛选出来的最佳药效团模型对活络丸中药化合物库进行虚拟筛选,对筛选到符合药效团模型的成分进行基于靶点结构的分子对接、结合自由能计算,选择潜在抑制成分进行酶活性测定实验,并对活性较好的小分子进行药代动力学、毒理学性质预测。结果构建的最佳药效团模型具有两个氢键受体和两个芳环中心;体外酶活性抑制实验结果显示,筛选出的11个潜在靶向抑制成分均对COX-2具有不同程度的抑制作用,其中抑制活性较强的成分为表儿茶素IC_(50)为(0.93±0.15)μmol·L^(-1)、木犀草素IC_(50)为(1.96±0.19)μmol·L^(-1)及槲皮素IC_(50)为(2.09±0.28)μmol·L^(-1),ADMET计算发现木犀草素、表儿茶素、槲皮素成药性较好。结论采用药效团模型、分子对接、结合自由能计算及体外酶活性抑制实验,挖掘活络丸中对COX-2有抑制作用的成分,为骨关节炎中药来源的单体成分的现代化研究提供线索。