分子识别在分析化学中的应用

本文概述了分子模板理论的产生和发展，总结了分子模板技术在分析化学领域中的应用和发展趋势，同时对分子印渍技术的理论进行概述，并指出分子印渍技术在分析化学领域中的应用和发展情况，阐述了分子模板和分子印渍技在分子识别分析方面的应用前景，其将为各种物质的超微量分析提供更加讯捷，准确，方便的分析方法。     

分子调控的概念及其意义

在分子识别的基础之上提出了分子调控的新概念，指出分子调控是外界因素对分子某些性质的指令性干预，是超分子体系所持有的功能，通过这种调控作用，可以有意识、有目的地控制分子的行为，并列举若干实例加以说明。     

一种新颖分子振荡元件的核磁共振研究

一种新颖分子振荡元件的核磁共振研究孙小强，何明阳，扬扬，孟启，何光裕（江苏石油化工学院应用化学系，常州，２１３０１６）关键词分子元件；核磁共振；超分子利用超分子自我识别、自我组装原理，在分子水平上研制、开发具有调控功能的分子元件已成为当前科研工作的前...     

可溶解的聚氨酯交联大分子的合成及其特殊的溶液行为

到目前为止，交联过程之所以难以控制是因为在交联聚合体系中，没有一个与交联过程相竞争的反应．本文利用大分子在溶液中是无规线团的特征，设计了含有分子内和分子间反应的交联聚合体系，分子间反应即交联过程，而分子内反应使分子问反应受到抑制，使交联过程的链增长不能无限发展，从而合成了可溶解的具有交联结构的大分子．     

分子整流器

分子尺寸器件是推动分子电子学发展的主力军。本文综述了分子整流器的工作原理，模型分子的设计，研究现状，存在的主要问题和今后的发展方向。     

新型主体化合物在生物活性分子分析中的应用新进展

主体化合物对客体分子具有高度的分子识别能力，对从分子水平上研究生物体内各种信息传递及酶与底物，抗体与抗原的结合等过程具有重要的理论与实际意义，它的应用为发展新型生物活性分子分析方法提供了广阔的前景，本文着重评述近年来冠醚，环糊精，杯芳烃及卟啉类超分子主体化合物在生物活性分子分析方面的研究进展。     

气相色谱中的超分子化学问题：Ⅰ.气相色谱与超分子化学的关系

超分子化学是有关超分子体系结构和功能的化学，超分子体系是由多个分子作用联系起来的实体，分子识别是形成超分本系的基本特征，本文从分子识别的角度，探讨了气相色谱学中超分子化学问题，并详细地评述了冠醚、液晶、环表固定液的分子识别机理的研究状况，最后，作者们大致展望了色谱研究超分子问题的前景，并且认为在多人工作基础上会产生一门新科学－超分子色谱学。     

分子印迹技术在毛细管电色谱中的应用

分子印迹技术是制备具有分子识别功能聚合物，即分子印迹聚合物（MIPs)的一种新技术；毛细管电色谱（CEC）是一个具有发展前途的色谱新技术。将分子印迹技术和毛细管电色谱两种新技术相结合，优势互补，具有极大的发展潜力。本文对分子印迹技术在毛细管电色谱中的应用，以及各类MIPs-CEC毛细管柱的制备方法进行了较为全面的综述，引用文献52篇。     

卟啉超分子化合物在分子器件中的应用

分子电子器件已成为近年来的一个研究热点，卟啉类化合物因为光敏性好、性能稳定、易于修饰等优点成为分子器件研究的理想模型化合物。本文着重介绍了它在分子器件中的最新应用进展。     

液晶态分子在分析化学中的应用进展

评述了液晶态分子在分析化学中的应用进展，包括其超分子的分子识别作用，液晶在色谱，光谱探针，核磁共振谱等分析化学领域中的应用。     

计算机辅助药物设计中的多维定量构效关系模型化方法

多维定量构效关系(MD QSAR)在计算机辅助药物设计中得到了广泛而成功的应用,结合本研究组的工作,本文系统综述了建立各种MD QSAR模型的方法和策略,对近年来有关的MD QSAR应用研究进展进行了回顾,并对其在新世纪药物设计中应用前景做了展望。     

Application of Mathematical Modeling and Computational Tools in the Modern Drug Design and Development Process

The conventional drug discovery approach is an expensive and time-consuming process, but its limitations have been overcome with the help of mathematical modeling and computational drug design approaches. Previously, finding a small molecular candidate as a drug against a disease was very costly and required a long time to screen a compound against a specific target. The development of novel targets and small molecular candidates against different diseases including emerging and reemerging diseases remains a major concern and necessitates the development of novel therapeutic targets as well as drug candidates as early as possible. In this regard, computational and mathematical modeling approaches for drug development are advantageous due to their fastest predictive ability and cost-effectiveness features. Computer-aided drug design (CADD) techniques utilize different computer programs as well as mathematics formulas to comprehend the interaction of a target and drugs. Traditional methods to determine small-molecule candidates as a drug have several limitations, but CADD utilizes novel methods that require little time and accurately predict a compound against a specific disease with minimal cost. Therefore, this review aims to provide a brief insight into the mathematical modeling and computational approaches for identifying a novel target and small molecular candidates for curing a specific disease. The comprehensive review mainly focuses on biological target prediction, structure-based and ligand-based drug design methods, molecular docking, virtual screening, pharmacophore modeling, quantitative structure–activity relationship (QSAR) models, molecular dynamics simulation, and MM-GBSA/MM-PBSA approaches along with valuable database resources and tools for identifying novel targets and therapeutics against a disease. This review will help researchers in a way that may open the road for the development of effective drugs and preventative measures against a disease in the future as early as possible.     

QSAR,Molecular Docking,MD Simulation and MMGBSA Calculations Approaches to Recognize Concealed Pharmacophoric Features Requisite for the Optimization of ALK Tyrosine Kinase Inhibitors as Anticancer Leads

ALK tyrosine kinase ALK TK is an important target in the development of anticancer drugs. In the present work, we have performed a QSAR analysis on a dataset of 224 molecules in order to quickly predict anticancer activity on query compounds. Double cross validation assigns an upward plunge to the genetic algorithm–multi linear regression (GA-MLR) based on robust univariate and multivariate QSAR models with high statistical performance reflected in various parameters like, fitting parameters; R² = 0.69–0.87, F = 403.46–292.11, etc., internal validation parameters; Q²_LOO = 0.69–0.86, Q²_LMO = 0.69–0.86, CCC_cv = 0.82–0.93, etc., or external validation parameters Q²_F1 = 0.64–0.82, Q²_F2 = 0.63–0.82, Q²_F3 = 0.65–0.81, R²_ext = 0.65–0.83 including RMSE_tr < RMSE_cv. The present QSAR evaluation successfully identified certain distinct structural features responsible for ALK TK inhibitory potency, such as planar Nitrogen within four bonds from the Nitrogen atom, Fluorine atom within five bonds beside the non-ring Oxygen atom, lipophilic atoms within two bonds from the ring Carbon atoms. Molecular docking, MD simulation, and MMGBSA computation results are in consensus with and complementary to the QSAR evaluations. As a result, the current study assists medicinal chemists in prioritizing compounds for experimental detection of anticancer activity, as well as their optimization towards more potent ALK tyrosine kinase inhibitor.     

聚电解质PSS/PDDA分子沉积膜表面性能研究

PSS PDDAMD膜紫外 可见吸光度与层数呈线性关系 ,其延长线基本为零证实了是一单分子层层状沉积过程 ;利用接触角测量仪跟踪MD膜沉积过程 ,其结果表明 ,层数较少时PSS PDDAMD膜表面润湿性呈“奇 偶”性规律变化 ,层数较多时规律性不明显 ,这说明聚电解质MD膜结构缺陷随着层数的增加有增大趋势 ;通过对原子力显微镜 (AFM)测定结果的分析 ,进一步证实了多层PSS PDDAMD膜存在结构缺陷 .     

QSAR Evaluations to Unravel the Structural Features in Lysine-Specific Histone Demethylase 1A Inhibitors for Novel Anticancer Lead Development Supported by Molecular Docking,MD Simulation and MMGBSA

Using 84 structurally diverse and experimentally validated LSD1/KDM1A inhibitors, quantitative structure–activity relationship (QSAR) models were built by OECD requirements. In the QSAR analysis, certainly significant and understated pharmacophoric features were identified as critical for LSD1 inhibition, such as a ring Carbon atom with exactly six bonds from a Nitrogen atom, partial charges of lipophilic atoms within eight bonds from a ring Sulphur atom, a non-ring Oxygen atom exactly nine bonds from the amide Nitrogen, etc. The genetic algorithm–multi-linear regression (GA-MLR) and double cross-validation criteria were used to create robust QSAR models with high predictability. In this study, two QSAR models were developed, with fitting parameters like R² = 0.83–0.81, F = 61.22–67.96, internal validation parameters such as Q²_LOO = 0.79–0.77, Q²_LMO = 0.78–0.76, CCC_cv = 0.89–0.88, and external validation parameters such as, R2ext = 0.82 and CCCex = 0.90. In terms of mechanistic interpretation and statistical analysis, both QSAR models are well-balanced. Furthermore, utilizing the pharmacophoric features revealed by QSAR modelling, molecular docking experiments corroborated with the most active compound’s binding to the LSD1 receptor. The docking results are then refined using Molecular dynamic simulation and MMGBSA analysis. As a consequence, the findings of the study can be used to produce LSD1/KDM1A inhibitors as anticancer leads.