卤键在化学传感和分子识别中的应用

卤键是一种新的分子间非共价作用力,它存在于卤素原子(路易斯酸)和具有孤电子对的原子或π-电子体系(路易斯碱)之间,在超分子化学、材料科学、生物识别和药物设计等领域已经显示出独特的优势。本文主要从卤键的特征和在化学传感和分子识别中的应用以及发展前景等几方面进行了介绍,期望引起人们对卤键的更多关注。     

锕系多重键金属有机化合物研究进展

多重键金属有机化合物在多种催化反应中已经得到广泛的应用, 并取得了很好的研究成果. 目前, 多重键金属有机化合物的研究已成为催化化学领域中一个引人注目的研究热点. 综述了锕系多重键金属有机化合物的研究进展. 根据化合物多重键的不同, 分别讨论了它们的合成及反应性能, 并定性讨论了多重键的性质.     

高位阻烷烃的C-C和C-H键的键离解能

本文使用ONIOM-G3B3的方法计算了一系列高位阻烷烃的C-C和C-H键离解能。研究还测定了它们的几何参数,如键长,键角,分子体积等,它们中的绝大多数分子目前还没有被合成。这些几何参数表征了位阻效应对键离解能产生的影响。研究确定了键离解能的迅速减小和分子体积的增大之间的一些关系。这些关系可以帮助使用理论方法预测很多高位阻化合物的合成。     

生物催化用于C—C键的立体选择性合成

C—C键的立体选择性形成是有机合成化学的重要方面. 生物催化剂的立体选择性是它们的主要优势之一, 用酶催化C—C键形成已引起了广泛关注. 总结了生物转化中C—C键形成的最新应用, 着重讨论了醛缩酶和转酮醇酶生物催化剂的应用.     

氨基酸修饰β-环糊精对胆酸分子键合的热力学性质及生物应用

环糊精对客体分子的选择键合是当今超分子化学的研究热点之一.其中,环糊精衍生物对胆酸类分子的键合行为及热力学性质研究对于理解主-客体识别机理及热力学起源具有重要的科学意义.本文主要综述了甘氨基、色氨酸、酪氨酸及其脱羧衍生物修饰的β-环糊精对几种典型胆酸分子的识别,包括胆酸(CA)、脱氧胆酸(DCA)、鹅去氧胆酸(CDCA)、甘胆酸(GCA)及牛黄胆酸(TCA)的键合模式、键合能力、分子选择性以及热力学起源,尤其是在清除体内胆酸、调控胆酸平衡、治疗疾病方面的应用.这些研究对于进一步深入理解主客体相互作用的识别机理以及推动超分子化学的发展都具有重要的作用.     

卤键在超分子化学中的应用进展

卤键是指作用在卤原子(路易斯酸)和具有孤对电子的原子或π电子体系(路易斯碱)之间的新型弱相互作用,其在超分子多维自组装和分子识别(如超分子催化、超分子选择拆分、超分子传感)等领域有着广泛的应用。本文介绍了卤键的类别、特性、功能及在超分子化学结构与功能领域中的应用。     

硫键：从概念形成到实际应用

早期含硫键物质的合成和硫键结构的发现为硫键概念形成奠定了基础。20世纪90年代以来,对硫族原子亲电性和亲核性的探索使人们对硫键的本质有了深刻的认识,促使了硫键概念的形成。此后,化学家分别于2002年和2010年开发了硫键超分子自组装和阴离子识别功能,并开始重视硫键在固体和溶液中的应用。随着对新型分子间作用力关注度的提高,硫键会越来越受到人们的重视,其应用也会有更广阔的前景。     

配位化学中的C-H…π非键弱相互作用

随着超分子化学研究的深入和发展,C-H…π非键弱相互作用越来越多地在晶体工程学、分子识别、主客体化学、自组装超分子体系以及生物大分子与配体相互作用等领域中被关注。本文综述了在配位化学中的C-H…π非键弱相互作用,它不仅存在于配合物分子内,亦可在配合物分子间观察到,并对利用配合物体系进行C-H…π非键弱相互作用体系研究提出一些设想。     

金属原子之间的键——过渡金属化学的新模式

自从第二次世界大战以来,过渡金属化学一直在无机化学中占据了主导地位。奇怪的是,经历了这么多年以后,这些元素仍然是化学研究中的丰富领域。不论在理论上或是实际应用上,过渡金属都是化学研究的前沿。在过渡金属化学的各个方面,最吸引人的是多核金属簇化合物。它们包含二个或更多的金属原子互相成键,以及和非金属元素成键。在这些金属簇中,称为Chevrel面的是迄今发现的,强磁场中最好的超导材料。其它簇可用为重要有机反应的催化剂。有些则是光敏络合物,在把太阳能转化为更有用的能量中,有潜在的作用。当然对很多化学家来说,这些金属簇之令人注意,只是因为它们的存在。它们中有的很稳定,易于制备,但只是在最近才对它们的化学开始有所了解。本报告是由这一领域中两位杰出的化学家撰写的。他们讨论了这些络合物以及可能的应用。     

价键理论新进展

概要介绍了现代价键理论的几个主要方法,并讨论了它们各自的特点及其发展现状,并重点介绍了键表方法的基本理论、计算程序及一些应用。     

Computational Characterization of Nanosystems

Nanosystems play an important role in many applications. Due to their complexity, it is challenging to accurately characterize their structure and properties. An important means to reach such a goal is computational simulation, which is grounded on ab initio electronic structure calculations. Low scaling and accurate electronic-structure algorithms have been developed in recent years. Especially, the efficiency of hybrid density functional calculations for periodic systems has been significantly improved. With electronic structure information, simulation methods can be developed to directly obtain experimentally comparable data. For example, scanning tunneling microscopy images can be effectively simulated with advanced algorithms. When the system we are interested in is strongly coupled to environment, such as the Kondo effect, solving the hierarchical equations of motion turns out to be an effective way of computational characterization. Furthermore, the first principles simulation on the excited state dynamics rapidly emerges in recent years, and nonadiabatic molecular dynamics method plays an important role. For nanosystem involved chemical processes, such as graphene growth, multiscale simulation methods should be developed to characterize their atomic details. In this review, we review some recent progresses in methodology development for computational characterization of nanosystems. Advanced algorithms and software are essential for us to better understand of the nanoworld.     

Molecular dynamics on distributed memory (MIMD) parallel computers

Summary Several algorithms are described that have been applied to molecular dynamics simulations and their merits discussed. The subject matter is confined to distributed (MIMD) algorithms. A simple mathematical model is used to illustrate the performance characteristics of a parallel MD algorithm.     

遗传算法在分析化学中的应用

遗传算法是基于自然界生物进化基本法进而发展起来的一类新算法，在优化过程中，它无需体系的选验知识，能在许多局部较优中找到全局最优点，是一种全局最优化方法，能有效地处理复杂的非线性问题，有广阔的发展前景，目前在分析化学领域已经有多方面应用，本文简要地介绍遗传算法的原理及其在分析化学等方面的若干应用。     

Clustering Algorithms to Analyze Molecular Dynamics Simulation Trajectories for Complex Chemical and Biological Systems?

Molecular dynamics (MD) simulation has become a powerful tool to investigate the structurefunction relationship of proteins and other biological macromolecules at atomic resolution and biologically relevant timescales. MD simulations often produce massive datasets containing millions of snapshots describing proteins in motion. Therefore, clustering algorithms have been in high demand to be developed and applied to classify these MD snapshots and gain biological insights. There mainly exist two categories of clustering algorithms that aim to group protein conformations into clusters based on the similarity of their shape (geometric clustering) and kinetics (kinetic clustering). In this paper, we review a series of frequently used clustering algorithms applied in MD simulations, including divisive algorithms, agglomerative algorithms (single-linkage, complete-linkage, average-linkage, centroid-linkage and ward-linkage), center-based algorithms (K-Means, K-Medoids, K-Centers, and APM), density-based algorithms (neighbor-based, DBSCAN, density-peaks, and Robust-DB), and spectral-based algorithms (PCCA and PCCA+). In particular, differences between geometric and kinetic clustering metrics will be discussed along with the performances of different clustering algorithms. We note that there does not exist a one-size-fits-all algorithm in the classification of MD datasets. For a specific application, the right choice of clustering algorithm should be based on the purpose of clustering, and the intrinsic properties of the MD conformational ensembles. Therefore, a main focus of our review is to describe the merits and limitations of each clustering algorithm. We expect that this review would be helpful to guide researchers to choose appropriate clustering algorithms for their own MD datasets.     

Experimental determination of the energy distribution of activated molecules. Extensive test of hard-collision approximations

The approximations developed to determine the energy distribution function of molecules activated above energy decomposition threshold, from experimental data, have been tested. The approach involved the theoretical (RRKM) calculations of “pseudoexperimental” data for a variety of activated energy distributions. (Single or double Gaussian representations were used in all cases.) Subsequently the algorithms mentioned were applied in order to recuperate the original (i.e., input) energy distributions from these pseudoexperimental data. The results obtained provide strong evidence in favor of the validity of the algorithms and illustrate the necessary requirements for their applications. A trend toward lower accuracy as the energy distributions move to higher energies has been observed. Evidence of the influence of the distribution width is also reported. The origins of the approximation errors have been studied, and ways for further improvement are suggested.     

Chemometrics applied to unravel multicomponent processes and mixtures: Revisiting latest trends in multivariate resolution

Progress in the analysis of multicomponent processes and mixtures relies on the combination of sophisticated instrumental techniques and suitable data analysis tools focused on the interpretation of the multivariate responses obtained. Despite the differences in compositional variation, complexity and origin, the raw measurements recorded in a multicomponent chemical system can be very often described with a simple model consisting of the composition-weighted sum of the signals of their pure compounds.

Multivariate resolution methods have been the tools designed to unravel this pure compound information from the non-selective mixed original experimental output. The evolution of these chemometric approaches through the improvement of exploratory tools, the adaptation to work with complex data structures, the ability to introduce chemical and mathematical information in the algorithms and the better quality assessment of the results obtained is revisited. The active research on these chemometric area has allowed the successful application of these methodologies to chemical problems as complex and diverse as the interpretation of protein folding processes or the resolution of spectroscopic images.     

Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms

We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.     

Automated conformational analysis and structure generation: algorithms for molecular perception

Many methodologies for performing automated conformational analysis require some means of "perceiving" a molecule to determine features of interest. Algorithms for finding rings, bond orders, and stereocenters and detecting the presence of substructural fragments have been developed. These algorithms are described, emphasizing their importance in conformational analysis.     

Bioinformatics resources and tools for phage display

Databases and computational tools for mimotopes have been an important part of phage display study. Five special databases and eighteen algorithms, programs and web servers and their applications are reviewed in this paper. Although these bioinformatics resources have been widely used to exclude target-unrelated peptides, characterize small molecules-protein interactions and map protein-protein interactions, a lot of problems are still waiting to be solved. With the improvement of these tools, they are expected to serve the phage display community better.     

Molecular mechanics study of myelin basic protein peptide 87-118: Some local energy minima

Myelin basic protein (MBP) is the major extrinsic protein of the myelin sheath in the central nervous system. It is this protein that is destroyed in such demyelinating diseases as multiple sclerosis. We have examined the predicted structures of one segment of MBP using the molecular mechanics program ECEPP83 developed by Scheraga and co-workers as modified by Chuman, Momany, and Schafer. We have focused upon a segment, 87-118, containing the Pro-Pro-Pro sequence (residues 100–102), which has been predicted from standard algorithms to exist in a hairpin loop connecting anti-parallel beta-strands. Several local energy minima have been found and reported. Tripoline sequences are not rare in proteins, but their structure and function is still uncertain.