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我国利用虚拟筛选技术研究抗 2型糖尿病新药取得进展中国科学院院士、中科院上海药物所所长、国家“973”新药创新研究项目首席科学家陈凯先在近日举行的“第八届海内外生命科学论坛”上介绍说 ,我国的科研人员利用虚拟筛选技术在抗 2型糖尿病新药的研究方面取得进展 ,基于国产     

艾滋病疫苗11月人体测试

中国科学院院士、中科院上海药物所所长、国家“973”新药创新研究项目首席科学家陈凯先在近日举行的“第八届海内外生命科学论坛”上介绍说,我国的科研人员利用虚拟筛选技术在抗2型糖尿病新药的研究方面取得进展,基于国产超级计算机的大规模虚拟筛选新药系统,目前已捕获数个具有显著胰岛素增敏作用的先导化合物,为有效治疗2型糖尿病展示了诱人前景。     

Autodock Vina与Discovery Studio在虚拟筛选耐药蛋白抑制剂中的比较

随着大量与细菌耐药相关的基因的发现和其表达蛋白结构的成功测定,从已有的化合物中通过计算机模拟方法筛选对耐药蛋白靶点有作用的候选化合物,成为了药物发现的一个标准途径。虚拟筛选在耐药基因抑制剂的发现中可以提高效率、降低实验成本。本文介绍了Autodock Vina和Discovery Studio在基于分子对接法的虚拟筛选中的使用,并对比分析其对β-内酰胺酶活性位点的筛选结果。希望通过这种比较促进虚拟筛选在药物设计领域中的应用,提高耐药基因抑制剂的发现速度。     

高通量药物筛选技术的研究现状及光学编码技术在其中的应用

高通量药物筛选是创新药物研究的重要内容,本文综合介绍了高通量药物筛选的研究现状及发展趋势,及目前遇到的一些问题,如随着样品的体积达到微升量级时,目前所采用的孔板筛选遇到了一些难以克服的障碍,使化合物和药物靶点的相互作用难以进行等。针对这些问题,本文介绍了一种新的以自编码光谱识别微球为基础的新型高通量药物筛选技术在高通量药物筛选中的应用,同时简要介绍了我们目前所做的一些工作。     

流感病毒神经氨酸酶抑制剂的筛选

以神经氨酸酶为药物靶点筛选神经氨酸酶抑制剂, 是研究和开发抗流感病毒药物的重要途径. 应用虚拟药物筛选方法, 从化合物数据库中选出部分待测化合物, 然后应用已建立的 神经氨酸酶抑制剂的高通量筛选模型, 检测了这些化合物的抑制活性, 从中发现了3个活性较高、结构新颖的化合物, 其IC50在0.1~3 μmol/L之间, 这些活性化合物的结构特点, 对于新型神经氨酸酶抑制剂的设计与开发, 将提供重要的信息指导. 流感病毒神经氨酸酶抑制剂的筛选结果表明, 虚拟筛选技术与高通量筛选技术的合理结合, 将有利于促进药物筛选与药物发现.     

高通量药物筛选技术进展及新药开发策略

新药研发过程中．通过筛选而获得具有生物活性的先导化合物．是创新药物研究的关键．目前药物筛选模型已经从传统的整体动物、器官和组织水平发展到细胞和分子水平。创新药物的发现都离不开采用适当的药物作用靶点对大量化合物样品进行筛选．而且筛选规模越大,发现新药的机会就越多。随着计算机技术、生物芯片、蛋白质组学、组合化学等的发展．高通量药物筛选技术应运而生。高通量筛选体系在创新药物筛选中的应用是新药开发研究的一个重要领域。     

计算机虚拟筛选技术在害虫行为调节剂研究中的应用

害虫行为调节剂是一种以嗅觉系统为靶标的绿色农药,在害虫的田间管理中发挥着重要的作用。然而,其先导化合物的发现通常依赖一系列生物测定的方法,不仅费时费力,且发现效率低。近年来,随着昆虫嗅觉功能数据的积累和结构生物学的飞速发展,以机器学习技术和分子对接为代表的2种基于计算机的药物虚拟筛选方法在害虫行为调节剂的先导化合物研究中发挥着重要的作用,极大地促进了先导化合物的发现效率,减少了筛选的盲目性。本文系统综述了2种虚拟筛选方法及其在害虫行为调节剂先导化合物研究中的应用,并对2种筛选策略在实际应用中存在的问题及应用前景进行了讨论。     

反向虚拟筛选平台及应用

靶标确证是老药新用、药物毒副作用研究的关键。基于分子对接方法 Auto Dock Vina和内部构建的疾病靶标数据库,采用分布式架构,构建了反向虚拟筛选平台。应用该平台对药物吡斯的明进行靶标确证,最终成功找到其靶标乙酰胆碱酯酶,验证了平台的实用性和准确性。     

基于受体的药物高通量筛选研究进展

受体是药物筛选的重要靶标,基于受体的药物高通量筛选是药物筛选的主要类型之一。本文根据受体作用原理,按照检测对象的不同,从直接与间接检测的角度,将基于受体的药物高通量筛选进行了分类,总结了基于受体的几种不同的药物筛选模型,并简要介绍了高通量筛选技术在中药研究中的应用,对药物筛选的发展进行了展望。     

代谢组学在肿瘤药物靶点筛选中的应用进展

肿瘤是一种多因素参与造成机体各系统功能平衡紊乱的代谢性疾病,代谢重编程是恶性肿瘤的重要特征之一。研究"代谢指纹图谱"的代谢组学,通过揭示肿瘤或药物引起的宿主内源性代谢物的变化,为肿瘤药物靶点的筛选提供了可能。但目前对代谢组在肿瘤药物靶点筛选中的整体性综述并不多见,因此,本文在介绍了代谢组学筛选肿瘤药物靶点的流程的基础上,然后依次对代谢组学在糖代谢、氨基酸代谢、脂质代谢等能量领域中肿瘤药物靶点筛选及其在揭示肿瘤耐药机制和靶向药物筛选中的应用进行了阐述,最后对代谢组学在肿瘤药物靶点研究中存在的问题以及未来发展趋势进行了探讨,以期为深入研究理解代谢组学在肿瘤机制和药物靶点发现中的重要作用提供参考和科学依据。     

Zika virus NS5 protein potential inhibitors: an enhanced in silico approach in drug discovery

The re-emerging Zika virus (ZIKV) is an arthropod-borne virus that has been described to have explosive potential as a worldwide pandemic. The initial transmission of the virus was through a mosquito vector, however, evolving modes of transmission has allowed the spread of the disease over continents. The virus has already been linked to irreversible chronic central nervous system conditions. The concerns of the scientific and clinical community are the consequences of Zika viral mutations, thus suggesting the urgent need for viral inhibitors. There have been large strides in vaccine development against the virus but there are still no FDA approved drugs available. Rapid rational drug design and discovery research is fundamental in the production of potent inhibitors against the virus that will not just mask the virus, but destroy it completely. In silico drug design allows for this prompt screening of potential leads, thus decreasing the consumption of precious time and resources. This study demonstrates an optimized and proven screening technique in the discovery of two potential small molecule inhibitors of ZIKV Methyltransferase and RNA dependent RNA polymerase. This in silico ‘per-residue energy decomposition pharmacophore’ virtual screening approach will be critical in aiding scientists in the discovery of not only effective inhibitors of Zika viral targets, but also a wide range of anti-viral agents.     

Docking, virtual high throughput screening and in silico fragment-based drug design

The drug discovery process has been profoundly changed recently by the adoption of computational methods helping the design of new drug candidates more rapidly and at lower costs. In silico drug design consists of a collection of tools helping to make rational decisions at the different steps of the drug discovery process, such as the identification of a biomolecular target of therapeutical interest, the selection or the design of new lead compounds and their modification to obtain better affinities, as well as pharmacokinetic and pharmacodynamic properties. Among the different tools available, a particular emphasis is placed in this review on molecular docking, virtual high-throughput screening and fragment-based ligand design.     

The use of chemical design tools to transform proteomics data into drug candidates

The Human Genome Project has fueled the massive information-driven growth of genomics and proteomics and promises to deliver new insights into biology and medicine. Since proteins represent the majority of drug targets, these molecules are the focus of activity in pharmaceutical and biotechnology organizations. In this article, we describe the processes by which computational drug design may be used to exploit protein structural information to create virtual small molecules that may become novel medicines. Experimental protein structure determination, site exploration, and virtual screening provide a foundation for small molecule generation in silico, thus creating the bridge between proteomics and drug discovery.     

Joint effects of acetochlor and urea on germinating characteristics of crop seeds

Neuraminidase (NA) is one of the most important targets to screen the drugs of anti-influenza virus A and B. After virtual screening approaches were applied to a compound database which possesses more than 10000 compound structures, 160 compounds were selected for bioactivity assay, then a High Throughput Screening (HTS) model established for influenza virus NA inhibitors was applied to detect these compounds. Finally, three compounds among them displayed higher inhibitory activities, the range of their IC5o was from 0.1 μmol/L to 3 μmol/L. Their structural scaffolds are novel and different from those of NA inhibitors approved for influenza treatment, and will be useful for the design and research of new NA inhibitors. The result indicated that the combination of virtual screening with HTS was very significant to drug screening and drug discovery.     

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.     

Drug screening for influenza neuraminidase inhibitors

Neuraminidase (NA) is one of the most important targets to screen the drugs of anti-influenza virus A and B. After virtual screening approaches were applied to a compound database which possesses more than 10000 compound structures, 160 compounds were selected for bioactivity assay, then a High Throughput Screening (HTS) model established for influenza virus NA inhibitors was applied to detect these compounds. Finally, three compounds among them displayed higher inhibitory activities, the range of their IC50 was from 0.1 μmol/L to 3μmol/L. Their structural scaffolds are novel and different from those of NA inhibitors approved for influenza treatment, and will be useful for the design and research of new NA inhibitors. The resuit indicated that the combination of virtual screening with HTS was very significant to drug screening and drug discovery.     

Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches

Virtual screening-based approaches to discover initial hit and lead compounds have the potential to reduce both the cost and time of early drug discovery stages, as well as to find inhibitors for even challenging target sites such as protein–protein interfaces. Here in this review, we provide an overview of the progress that has been made in virtual screening methodology and technology on multiple fronts in recent years. The advent of ultra-large virtual screens, in which hundreds of millions to billions of compounds are screened, has proven to be a powerful approach to discover highly potent hit compounds. However, these developments are just the tip of the iceberg, with new technologies and methods emerging to propel the field forward. Examples include novel machine-learning approaches, which can reduce the computational costs of virtual screening dramatically, while progress in quantum-mechanical approaches can increase the accuracy of predictions of various small molecule properties.     

A biased ligand for OXE-R uncouples Gα and Gβγ signaling within a heterotrimer

Differential targeting of heterotrimeric G protein versus β-arrestin signaling are emerging concepts in G protein-coupled receptor (GPCR) research and drug discovery, and biased engagement by GPCR ligands of either β-arrestin or G protein pathways has been disclosed. Herein we report on a new mechanism of ligand bias to titrate the signaling specificity of a cell-surface GPCR. Using a combination of biomolecular and virtual screening, we identified the small-molecule modulator Gue1654, which inhibits Gβγ but not Gα signaling triggered upon activation of Gα(i)-βγ by the chemoattractant receptor OXE-R in both recombinant and human primary cells. Gue1654 does not interfere nonspecifically with signaling directly at or downstream of Gβγ. This hitherto unappreciated mechanism of ligand bias at a GPCR highlights both a new paradigm for functional selectivity and a potentially new strategy to develop pathway-specific therapeutics.     

Structure-based virtual screening of chemical libraries for drug discovery

One of the main goals in drug discovery is to identify new chemical entities that have a high likelihood of binding to the target protein to elicit the desired biological response. To this end, virtual screening is being increasingly used as a complement to high-throughput screening to improve the speed and efficiency of the drug discovery and development process. The availability of inexpensive high-performance computing platforms in recent years has transformed this field into one that is highly diverse and rapidly evolving, where large chemical databases have been successfully screened to identify hits for a wide range of targets such as Bcl-2 family proteins, G protein-coupled receptors, kinases, metalloproteins, nuclear hormone receptors, proteases and many more.