利用原卟啉原氧化酶抑制剂药效团模型进行数据库搜索

为了设计与合成新型原卟啉原氧化酶抑制剂，利用所建立的药效团模型为基础，建立提问结构，将药效团模型中的生物结构信息输入到多种小分子三维结构数据库中(NCI-3D和ACD-3D数据库)，借助分子三维结构搜索软件(3DFS和ISIS／3D软件)和多种搜索方法(POWELL方法和遗传算法)分别搜寻出一百多个符合特征结构信息的全新结构候选化合物。     

距离比较法构建酪氨酸酶抑制剂的药效团模型

运用距离比较法程序建立了酪氨酸酶抑制剂的药效团模型，以此为提问结构对Maybridge和NCI，2个数据库进行搜索，得到了一系列新型的可能具有高活性的黑色素合成抑制剂。     

原卟啉原氧化酶抑制剂的三维药效团研究

在靶酶三维结构未知的条件下，采用限制性系统搜索方法（Constrained Systematic Search），对5类典型的原卟啉原氧化酶（Protox）抑制剂的三维药效团进行了研究，发现这些抑制剂共同的活性部位可能是2个心水中心和一个氢键接受体，得到的药效团模型对在分子和原子水平探讨不同结构类型的原卟啉原氧化酶抑制剂与靶酶相互作用的共同机制，指导新型抑制剂的设计与合成等方面具有重要的理论指导意义和潜在的实际应用价值。     

HMG-CoA还原酶的活性位点分析及其抑制剂药效团模型的构建

HMG-CoA还原酶(HMG-CoA Reductase,HMGR)是降血脂药物设计的重要靶标,抑制该酶的活性可以有效地降低血浆总胆固醇水平,从而降低心脑血管疾病的发病几率。虽然已经开发了数种他汀类药物作为HMGR抑制剂应用于临床,但是他汀类药物的安全性,特别是长期服用的安全性一直备受关注,所以设计新型安全的HMGR抑制剂仍然十分迫切。本论文利用蛋白质活性位点分析程序Grid,分析了HMGR底物结合腔的形状和表面特性,在细致地分析了各类药物与HMGR具体的氢键、疏水相互作用后,结合分子对接、3D-QSAR研究结果,总结了HMGR抑制剂的药效基团模型,并提出了可行的HMGR抑制剂的设计方案,为全新HMGR抑制剂的设计和先导化合物的优化提供了可靠的信息,并对HMGR抑制剂的进一步修饰提出了可行的思路。     

3D-QSAR analysis of TRPV1 inhibitors reveals a pharmacophore applicable to diverse scaffolds and clinical candidates

TRPV1 (Transient Receptor Potential Vanilloid Type 1) receptor, a member of Transient Receptor Potential Vanilloid subfamily of ion channels, occurs in the peripheral and central nervous system, and plays a key role in transmission of pain. Consequently, this has been the target for discovery of several pain relieving agents which have undergone clinical trials. Though several TRPV1 antagonists have progressed to become clinical candidates, many are known to cause temperature elevation in humans, halting their further advancement, and signifying the need for new chemotypes. Different chemical classes of TRPV1 antagonists share three important features: an amide or an isostere flanked by an aromatic (or fused aromatic) ring with polar substitutions on one side, and a hydrophobic group on the other. Recent work identified new series of compounds with these and additional features, leading to improvement of properties, and development of clinical candidates. Herein, we describe a 3D-QSAR model (n = 62; R² = 0.9 and Q² = 0.75) developed from the piperazinyl-aryl series of compounds and a novel 5-point pharmacophore model is shown to fit several diverse scaffolds, six clinical candidates, five pre-clinical candidates and three lead compounds. The pharmacophore model can aid in finding new chemotypes as starting points that can be developed further.     

From activity cliffs to target-specific scoring models and pharmacophore hypotheses

The role of activity cliffs in drug discovery projects is certainly two-edged: on the one hand, they often lead to the failure of QSAR modeling techniques; on the other, they are highly valuable for identifying key aspects of SARs. In the presence of activity cliffs the results of purely ligand-based QSAR approaches often remain puzzling, and the resulting models have limited predictive power. Herein we present a new approach for the identification of structure-based activity cliffs (ISAC). It uses the valuable information of activity cliffs in a structure-based design scenario by analyzing interaction energies of protein-ligand complexes. Using the relative frequency at which a protein atom is involved in activity cliff events, we introduce a novel visualization of hot spots in the active site of a protein. The ISAC approach supports the medicinal chemist in elucidating the key interacting atoms of the binding site and facilitates the development of pharmacophore hypotheses. The hot spot visualization can be applied to small data sets in early project phases as well as in the lead optimization process. Based on the ISAC approach, we developed a method to derive target-specific scoring functions and pharmacophore constraints, which were validated on independent external data sets in virtual screening experiments. The activity-cliff-based approach shows an improved enrichment over the generic empirical scoring function for various protein targets in the validation set.     

Combined Structure-Based Pharmacophore and 3D-QSAR Studies on Phenylalanine Series Compounds as TPH1 Inhibitors

Tryptophan hydroxylase-1 (TPH1) is a key enzyme in the synthesis of serotonin. As a neurotransmitter, serotonin plays important physiological roles both peripherally and centrally. In this study, a combination of ligand-based and structure-based methods is used to clarify the essential quantitative structure-activity relationship (QSAR) of known TPH1 inhibitors. A multicomplex-based pharmacophore (MCBP) guided method has been suggested to generate a comprehensive pharmacophore of TPH1 kinase based on three crystal structures of TPH1-inhibitor complex. This model has been successfully used to identify the bioactive conformation and align 32 structurally diverse substituted phenylalanine derivatives. The QSAR analyses have been performed on these TPH1 inhibitors based on the MCBP guided alignment. These results may provide important information for further design and virtual screening of novel TPH1 inhibitors.     

Pharmacophore and Molecular Docking Guided 3D-QSAR Study of Bacterial Enoyl-ACP Reductase (FabI) Inhibitors

Enoyl acyl carrier protein (ACP) reductase (FabI) is a potential target for the development of antibacterial agents. Three-dimensional quantitative structure-activity relationships (3D-QSAR) for substituted formamides series of FabI inhibitors were investigated using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques. Pharmacophore and molecular docking methods were used for construction of the molecular alignments. A training set of 36 compounds was performed to create the 3D-QSAR models and their external predictivity was proven using a test set of 11 compounds. Graphical interpretation of the results revealed important structural features of the formamides related to the active site of FabI. The results may be exploited for further optimization of the design of new potent FabI inhibitors.