Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore （LBP） and structure-based pharmacophore （SBP）. LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distin- guishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore （MP） is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of per- oxisome proliferator receptor-alpha （PPARα） agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.
肝X受体β(liver X receptorβ,LXRβ)与体内胆固醇代谢密切相关,是治疗高脂血症的药物新靶点。该文以LXRβ激动剂为载体,利用3D-QSAR pharmacophore(Hypogen)模块构建定量药效团,得到最优的药效团模型包含1个氢键受体,1个芳环基团和2个疏水基团,药效团的5项评价指标分别为:训练集化合物的预测活性值和实验活性值的相关系数(correlation)为0.95、模型的费用函数(Δcost值)为128.65、活性化合物有效命中率(HRA)为84.44%、辨识有效性指数(IEI)为2.58、综合评价指数(CAI)为2.18。利用最优药效团模型筛选中药化学成分数据库(traditional Chinese medicine database,TCMD),初步获得309个潜在的中药活性成分。随后利用Libdock分子对接方法进一步精制筛选结果,基于原配体的打分值以及关键氨基酸建立筛选规则,最终得到去甲氧基姜黄素、异甘草黄酮醇、胀果甘草查尔酮E、水飞蓟宁4个化合物为潜在的LXRβ激动剂。该研究可以高效、低耗的筛选潜在的LXRβ中药激动剂,为抗高血脂新药研发提供助力。
多靶点药物能同时调节多靶点、调节疾病网络的多个环节,在获得较高疗效的同时可降低单靶点引起的毒副作用,是治疗复杂性疾病的理想药物,因此已成为药物研发的主要方向。而天然产物凭借其结构的多样性,较高的多靶点活性和较小的毒副作用等优势,是多靶点药物开发的重要来源。计算机辅助药物设计(computer-aided drug design,CADD)是常用的多靶点药物研发方法,其主要包括虚拟筛选和药效团设计。该文对其进行了系统梳理,探讨了各方法用于天然产物多靶点药物研发的前景与优势。
