动态组合化学最新进展

动态组合化学是组合化学的一个新兴分支,在药物先导化合物的发现中有广阔的应用前景。在动态组合化学库中,利用靶标分子的诱导结合作用,通过可逆共价反应,能够选择性的筛选到与靶标分子存在强相互作用的优势化合物。本文按照动态组合化学方法简介、动态组合化学中的可逆共价化学、动态组合化学库的分类、动态组合化学库筛选方法的研究进展及动态组合化学在药物先导化合物发现过程中的应用等5个方面对动态组合化学进行了概述。     

动态组合化学研究进展及其在药物设计中的应用

动态组合化学利用可逆过程连接库内的各种组分,实现动态库的多样性.在库中加入靶标分子,通过分子识别、分子组装,诱导产生和靶标分子产生最强键合的化合物,推动化合物库的移动.介绍了动态组合化学的基本原理,并综述了近年来发现的动态可逆过程和动态组合化学在生物学、新药设计中的应用.     

蛋白质的核酸适配体筛选的研究进展

核酸适配体是通过指数富集系统配体进化(SELEX)筛选获得的,与靶标具有高亲和力和特异性结合的单链DNA或RNA。蛋白质是生命进程中的关键功能分子。近年来,以蛋白质为靶标的适配体筛选在蛋白质相关的基础及应用研究领域受到广泛关注。核酸适配体应用性能的优劣取决于其亲和力、特异性与稳定性。目前,适配体筛选方法的优化主要是提高筛选效率、提升适配体性能及降低筛选成本。适配体主要筛选步骤包括复合物分离、核酸库优化、次级库的富集、适配体序列分析以及亲和力表征等。迄今为止,以蛋白质-核酸复合物的分离为核心步骤的适配体筛选方法有20余种。本文归纳总结了2005年以来以蛋白质为靶标的适配体筛选技术,讨论了各方法的缺陷与局限。介绍了核酸库的设计优化方法、适配体的序列特征,以及常用的亲和力表征方法。     

小分子靶标的核酸适配体筛选的研究进展

核酸适配体(aptamer)是通过指数富集配体系统进化(SELEX)技术筛选得到的核糖核酸(RNA)或单链脱氧核糖核酸(ssDNA)。核酸适配体通过高亲和力特异性地识别小分子、蛋白质、细胞、微生物等多种靶标,在生物、医药、食品和环境检测等领域的应用日渐增多。但目前实际可用的核酸适配体有限,其筛选过程复杂,筛选难度大,制约了其应用。与生物大分子、细胞和微生物等靶标不同,小分子靶标与核酸分子的结合位点少、亲和力弱,且靶标通常需要固定在载体上。此外,小分子靶标结合核酸形成的复合物与核酸自身的大小、质量、电荷性质等方面差异较小,二者的分离难度大。故小分子靶标的核酸适配体筛选过程与大分子和细胞等复合靶标相比有明显差异,筛选难度更大。因此需要根据其自身结构特点和核酸适配体的应用目的选定靶标或核酸库的固定方法,优化靶标核酸复合物的分离方法。本文介绍了不同类型小分子(具有基团差异的单分子、含相同基团分子和手性分子等)靶标的选择及其核酸适配体的筛选方法,并对核酸库的设计、与靶标结合的核酸的分离方法和亲和作用表征方法进行了介绍,列出了自2008年以来报道的40余种小分子靶标的核酸适配体序列和复合物的平衡解离常数(K_d)。     

多肽定量构效关系与分子设计

综述了多肽定量构效关系和计算机辅助多肽分子设计方法的最新进展,重点介绍了多肽定量构效关系研究中的化学结构定量描述符和建立数学模型的统计方法,并对模拟肽学和虚拟组合多肽库在多肽分子设计中的应用进行了简要的论述.     

清除树脂在组合化学中的应用

清除树脂是一种特殊的固相试剂。近五年，它们被广泛的用于平行液相法合成组合化学库，简化液相组合化学库的纯化操作。本文综述了清除树脂在纯化操作中的使用策略。根据清除纯化原理，介绍了共价键型和离子键型两类清除树脂。给出了清除树脂在组合化学中的具体应用实例。最后对清除树脂的发展作了展望。     

化合物库的组合技巧和组合合成

从化合物库的组合技巧和组合合成方法两个方面探讨了组合化学及其在药物开发中的应用。     

化学信息学的涵义及教育

化学信息学是近年来发展起来的新学科 ,它的产生与发展是基于化学信息量指数般增长 ,特别是组合化学及高通量筛选的迅速发展。组合化学方法能像搭积木块一样快速合成及制备大量的化合物。一个组合化学库包括数百个至数十万个化合物 ,为药物开发提供丰富的化合物源。高通量筛选能达到 1× 1 0 4～ 1× 1 0 5 个化合物 /天。组合化学及高通量筛选为药物研制提供新的技术支柱 ,同时也为化学信息学的产生与发展提供良好的机遇。　　人类基因组计划为药物开发与疾病的治疗提供众多的新靶标。据 1 996年统计用于药物研制的靶标有 483个分子靶 (其…     

超分子双膦配体的构筑及应用研究进展

超分子双膦配体是一类新兴起的基于非共价键作用构筑的双膦配体,近年来引起人们的重视.与传统的共价键连接的双膦配体相比,利用非共价相互作用的可逆性和选择性,超分子双膦配体具有合成简便,组合灵活,易于合成超分子配体库,并利用组合化学的方法对催化体系进行优化和筛选等优点.详细综述了近几年发展的基于氢键、配位键、主客体作用和静电作用等弱相互作用的超分子双膦配体,重点讨论了它们的构建方法以及在不对称催化反应中的应用,并对其发展前景进行了展望.     

固态组合化学及其在材料科学中的应用

概述了固态组合化学的概念及其在新材料开发中的应用 ,着重阐述了材料化合物薄膜库的组合制备方法和筛选技术的特点 ,简述了固态组合化学技术在超导、巨磁阻、磷光、催化剂、沸石等新材料开发中的应用以及固态组合化学现有技术中的困难和今后的发展方向     

Application of self-organizing maps in compounds pattern recognition and combinatorial library design

In the computer-aided drug design, in order to find some new leads from a large library of compounds, the pattern recognition study of the diversity and similarity assessment of the chemical compounds is required; meanwhile in the combinatorial library design, more attention is given to design target focusing library along with diversity and drug-likeness criteria. This review presents the current state-of-art applications of Kohonen self-organizing maps (SOM) for studying the compounds pattern recognition, comparing the property of molecular surfaces, distinguishing drug-like and nondrug-like molecules, splitting a dataset into the proper training and test sets before constructing a QSAR (Quantitative Structural-Activity Relationship) model, and also for the combinatorial libraries comparison and the combinatorial library design. The Kohonen self-organizing map will continue to play an important role in drug discovery and library design.     

An ontology for pharmaceutical ligands and its application for in silico screening and library design

Annotation efforts in biosciences have focused in past years mainly on the annotation of genomic sequences. Only very limited effort has been put into annotation schemes for pharmaceutical ligands. Here we propose annotation schemes for the ligands of four major target classes, enzymes, G protein-coupled receptors (GPCRs), nuclear receptors (NRs), and ligand-gated ion channels (LGICs), and outline their usage for in silico screening and combinatorial library design. The proposed schemes cover ligand functionality and hierarchical levels of target classification. The classification schemes are based on those established by the EC, GPCRDB, NuclearDB, and LGICDB. The ligands of the MDL Drug Data Report (MDDR) database serve as a reference data set of known pharmacologically active compounds. All ligands were annotated according to the schemes when attribution was possible based on the activity classification provided by the reference database. The purpose of the ligand-target classification schemes is to allow annotation-based searching of the ligand database. In addition, the biological sequence information of the target is directly linkable to the ligand, hereby allowing sequence similarity-based identification of ligands of next homologous receptors. Ligands of specified levels can easily be retrieved to serve as comprehensive reference sets for cheminformatics-based similarity searches and for design of target class focused compound libraries. Retrospective in silico screening experiments within the MDDR01.1 database, searching for structures binding to dopamine D2, all dopamine receptors and all amine-binding class A GPCRs using known dopamine D2 binding compounds as a reference set, have shown that such reference sets are in particular useful for the identification of ligands binding to receptors closely related to the reference system. The potential for ligand identification drops with increasing phylogenetic distance. The analysis of the focus of a tertiary amine based combinatorial library compared to known amine binding class A GPCRs, peptide binding class A GPCRs, and LGIC ligands constitutes a second application scenario which illustrates how the focus of a combinatorial library can be treated quantitatively. The provided annotation schemes, which bridge chem- and bioinformatics by linking ligands to sequences, are expected to be of key utility for further systematic chemogenomics exploration of previously well explored target families.     

液相组合化学

综述了液相组合化学的研究进展，重点介绍了液相组合合成中的分离纯化方法和合成方法策略，基本分离纯化方法包括利用固相载体协助分离纯化法，相萃取分离纯化法和色谱法，主要合成方法策略有平行合成策略和索引合成策略。     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.     

A general method for designing combinatorial peptide libraries decodable by amino acid analysis

Herein we describe an algorithm for designing combinatorial peptide libraries for split-and-mix synthesis on solid support that are decodable by amino acid analysis (AAA) of the beads. AAA is a standard service analysis available in most biochemical laboratories, and it allows one to control the quality of the peptide on each bead, an important feature that is missing from most library decoding protocols. In the algorithm, each AA is assigned to two variable positions in the sequence grouped in a "unique pair". This arrangement limits sequence design because both the number of unique pairs U (setting the maximum number of variable AA) and the maximum number S of different AA per variable position depend on the peptide length N (U=N(N-1)/2), S=N-1). The method is therefore only suitable for focused libraries. An application example is shown for the selection of peptides with N-terminal proline or hydroxyproline catalyzing an aldol reaction from a combinatorial library of 65536 octapeptides. A simple enumeration program is available to help design combinatorial libraries decodable by amino acid analysis. The method applies to linear and cyclic peptides, can be used for nonnatural building blocks, including beta-amino acids, and should help to explore the vast chemistry of linear and cyclic peptide for catalysis and bioactivity.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

Combinatorial library design using a multiobjective genetic algorithm

Early results from screening combinatorial libraries have been disappointing with libraries either failing to deliver the improved hit rates that were expected or resulting in hits with characteristics that make them undesirable as lead compounds. Consequently, the focus in library design has shifted toward designing libraries that are optimized on multiple properties simultaneously, for example, diversity and "druglike" physicochemical properties. Here we describe the program MoSELECT that is based on a multiobjective genetic algorithm and which is able to suggest a family of solutions to multiobjective library design where all the solutions are equally valid and each represents a different compromise between the objectives. MoSELECT also allows the relationships between the different objectives to be explored with competing objectives easily identified. The library designer can then make an informed choice on which solution(s) to explore. Various performance characteristics of MoSELECT are reported based on a number of different combinatorial libraries.     

Library Design Guided by In-house Developed Computer Tools

In recent years, combinatorial library synthesis for drug discovery begins to migrate from library synthesis solely dictated by chemistry availability to design and synthesis of libraries with more drug-like properties. Lipinski's rule of five has been used to evaluate drug-like properties of individual compound; recently LibProTM, a new computation program has been developed at Pharmacopeia to evaluate durg-like properties of libraries. By using LibPrpTM, chemists at Pharmacopeia are able to obtain information of molecular weight and ClogP distribution of a library, and percentage of library members that violate Lipinski's rule after input structures of synthons for each combinatorial step. Currently, a "virtual library design” approach that is to calculate properties of a library at conceptual phase of the library design has been used to predetermine the value of the library. Also a new computer program used to predict "Absorption” of compounds will also be discussed.