多组分缩合反应合成肽核酸类化合物.Ⅰ.肽核酸单体的合成

目的 对新型反义核酸类化合物肽核酸（PNA）进行结构改造。探索并利用多组分缩合反应（Ugi反应）进行PNA类似物单体的合成。方法 改变了一般合成PNA的思路，采用Ugi反应进行PNA的合成。结果 设计并合成了Ugi反应中的关键组分：异腈，PNA单体。结论 所得产物经MS，IR，^1H-NMR及元素分析确证与目标化合物相符。该反应具有步骤简单，基团变换灵活的优点。     

Mitsunobu反应及其在药物化学中的应用

介绍了Mitsunobu反应的基本概况和研究新进展。举例说明了本反应在药物化学中的应用，重点放在药物及天然产物合成和构型反转上。     

组合化学及其在新药研究中的应用

介绍了组合化学的一些基本概念、化合物库的合成方法及筛选策略，以及组合化学的发展。     

高效合成技术在药物研发中的应用

化合物库的构建和化合物的合成是药物研发的重要部分,应用高效的有机合成技术,可以大大加快药物研发的进程。本文从"快速"、"复杂"和"多样性"3个方面,介绍了微波化学、点击化学、组合化学、串联反应和多组分反应等高效合成技术在药物研发中的应用,以及多样性化合物库的构建。     

微波技术在药物合成反应中的应用研究：Ⅰ.环缩合反应初探

采用微波辐射加热新技术，对环缩合反应进行了初步研究，合成了乙亚胺，喹唑酮－４，双酮酞嗪和二羟基哒嗪 等杂环药物合成中间体。与传统方法相比，产率接近，但其反应速度极大地加快，反应时间从几小时缩短为几分钟相差几十倍－几百倍。     

药物发现中的化学合成新策略

为了在短时间内发现性质优良的药物，药物化学家们需要对早期药物研发过程的效率和通量进行优化，包括利用新技术简化合成步骤及利用突破性反应来处理更为复杂的合成问题。本文主要讨论了部分实用的新技术，如聚合物辅助液相合成、微波辅助有机合成、流动化学等，并介绍了几种已被列入常规反应的突破性合成方法，如点击化学反应，多组分反应和关环易位反应。     

氟化反应在药物合成中的应用

由于在药物中引入氟原子或含氟基团能够使药效增强、作用持久、副作用降低,因此,近年来含氟医药的开发研究倍受关注。本文重点对含氟芳香化合物的制备方法及其在药物中间体,特别是在喹诺酮类药物合成中的应用进行了详细介绍。通过含氟芳香化合物可以在喹诺酮类药物的5位、6位、8位引入氟原子,同时还能在1位氮原子上引入含氟芳香化合物如对氟苯基或2,4-二氟苯基。最后对有机氟化学新反应进行了介绍,即亚磺化脱卤反应、金属及过渡金属引发的有机氟化学反应以及氧化还原体系引发下氟卤烃的化学研究。这些新的研究成果为把含氟基团引入有机分子提供了有效方法,对合成新型含氟医药具有重要意义。     

Negishi偶联反应在沙坦类药物联苯基合成中的应用

目的：探索沙坦类药物新的合成方法,简化其合成步骤。方法：以价廉易得的化合物为原料,合成中间体N-戊酰基-N-[（4-溴）-苯甲基]-L-缬氨酸甲酯和5-苯基-1-三苯甲基四氮唑,并经Negishi偶联合成目标化合物缬沙坦。结果与结论：目标物的化学结构经核磁共振氢谱、碳谱、质谱、红外光谱和元素分析确证。合成工艺的总收率为55%。     

微波技术在药物合成反应中的应用研究：Ⅱ.无溶剂条件下酚羟基…

在无溶剂条件下，以硫酸二甲酯作为甲基化试剂，采用微波辐射技术进行酚羟基的甲基化反应，合成了４个含甲氧基化合物药物中间体，该方法具有操作简单，反应时间短和成本低等优点，有一定实用意义。     

药物发现中的定向多样性合成

本文介绍了定向多样性合成的概念、在药物发现中的作用及实现分子复杂性和多样性的方法.     

多组分生化药致敏性评价策略研究进展

多组分生化药因来源复杂、物质基础不明,部分品种临床应用中不良反应率较高而被录入国家重点监控药品目录.虽然其不良反应多涉及过敏样反应,但是目前对多组分生化药的过敏性研究甚少,尚未形成完善的过敏性研究评估体系.故本文通过对多组分生化药的特点及近年来国内外复杂机制类药物过敏性研究相关文献进行分析,阐述了适用于多组分生化药的过...     

"Multi-component reactions : emerging chemistry in drug discovery" 'from xylocain to crixivan'

With the recent emergence of combinatorial chemistry and high-speed parallel synthesis for drug discovery applications, the multi-component reaction (MCR) has seen a resurgence of interest. Easily automated one-pot reactions, such as the Ugi and Passerini reactions, are powerful tools for producing diverse arrays of compounds, often in one step and high yield. Despite this synthetic potential, the Ugi reaction is limited by producing products that are flexible and peptide-like, often being classified as 'non drug-like'. This review details developments of new, highly atom-economic MCR derived chemical methods, which enable the fast and efficient production of chemical libraries comprised of a variety of biologically relevant templates. Representative examples will also be given demonstrating the successful impact of MCR combinatorial methods at different stages of the lead discovery, lead optimization and pre-clinical process development arenas. This will include applications spanning biological tools, natural products and natural product-like diversity, traditional small molecule and 'biotech' therapeutics respectively. In particular, this review will focus on applications of isocyanide based MCR (IMCR) reactions.     

Contemporary medicinal-chemistry strategies for discovery of blood coagulation factor Xa inhibitors

ABSTRACT

Introduction: Thrombosis is a common causal pathology for stroke, acute coronary syndrome and venous thromboembolism disorders, which are the leading cause of death worldwide. Anticoagulants have exhibited a crucial role in the prevention and treatment of thrombotic diseases. Factor Xa (FXa) is a serine protease with a central role in activating the complex blood coagulation cascade, and it is therefore regarded as an attractive target for antithrombotic agents.

Areas covered: The authors review the current status of medicinal chemistry strategies for the discovery of novel FXa inhibitors and provide their expert perspectives on their future development.

Expert opinion: Even if only a number of small-molecule FXa inhibitors have been reported to date, all currently available FXa inhibitors are associated with significant risk of bleeding, which may become life-threatening. There is, therefore, an urgent and unmet demand for potent novel FXa inhibitors that are potent treatments for thrombotic disorders, but which have a reduced risk of bleeding if their use is to be increasingly favored.     

Animal inflammation-based models of depression and their application to drug discovery

Introduction: Depression, anxiety and other affective disorders are globally widespread and severely debilitating human brain diseases. Despite their high prevalence and mental health impact, affective pathogenesis is poorly understood, and often remains recurrent and resistant to treatment. The lack of efficient antidepressants and presently limited conceptual innovation necessitate novel approaches and new drug targets in the field of antidepressant therapy.

Areas covered: Herein, the authors discuss the emerging role of neuro-immune interactions in affective pathogenesis, which can become useful targets for CNS drug discovery, including modulating neuroinflammatory pathways to alleviate affective pathogenesis.

Expert opinion: Mounting evidence implicates microglia, polyunsaturated fatty acids (PUFAs), glucocorticoids and gut microbiota in both inflammation and depression. It is suggested that novel antidepressants can be developed based on targeting microglia-, PUFAs-, glucocorticoid- and gut microbiota-mediated cellular pathways. In addition, the authors call for a wider application of novel model organisms, such as zebrafish, in studying shared, evolutionarily conserved (and therefore, core) neuro-immune mechanisms of depression.     

Target-drug interactions: first principles and their application to drug discovery

In this review, we begin by introducing the basic principles of kinetics and thermodynamics of target-drug binding within the context of drug discovery. In addition, we present a meta-analysis of the recent literature describing the kinetic and thermodynamic resolution of successful clinical candidates with diverse mechanisms of action. We finish by discussing the best practices in the triage and chemical optimization towards clinical candidates with maximal in vivo efficacy devoid of adverse events.     

Stabilization of protein-protein interactions in drug discovery

Introduction: PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery.

Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach.

Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.     

他汀类药物的不良反应

他汀类药物是临床广泛应用的降脂药,在多种心血管疾病的治疗中发挥重要作用。随着使用范围和使用人数的增加,其不良反应报道逐渐增多,包括他汀类药物相关性肌病、肝损害、过敏反应、眼外肌麻痹、性功能障碍、多尿、消化系统反应、关节痛、下肢不宁综合征等。本文对他汀类药物的不良反应和防治措施进行简要综述,以促进其合理应用。     

Advances with support vector machines for novel drug discovery

Introduction: Novel drug discovery remains an enormous challenge, with various computer-aided drug design (CADD) approaches having been widely employed for this purpose. CADD, specifically the commonly used support vector machines (SVMs), can employ machine learning techniques. SVMs and their variations offer numerous drug discovery applications, which range from the classification of substances (as active or inactive) to the construction of regression models and the ranking/virtual screening of databased compounds.

Areas covered: Herein, the authors consider some of the applications of SVMs in medicinal chemistry, illustrating their main advantages and disadvantages, as well as trends in their utilization, via the available published literature. The aim of this review is to provide an up-to-date review of the recent applications of SVMs in drug discovery as described by the literature, thereby highlighting their strengths, weaknesses, and future challenges.

Expert opinion: Techniques based on SVMs are considered as powerful approaches in early drug discovery. The ability of SVMs to classify active or inactive compounds has enabled the prioritization of substances for virtual screening. Indeed, one of the main advantages of SVMs is related to their potential in the analysis of nonlinear problems. However, despite successes in employing SVMs, the challenges of improving accuracy remain.