计算机自动结构解析专家系统研究──一个新的拓扑等价性识别算法

介绍了一个正确识别化合物结构或结构片断中各原子的拓扑等价性问题的新算法。算法中引入了节点矩阵Ｎ和键矩阵Ｂ，并由这两个新矩阵来计算化合物结构图中的各节点的特征值，以此来表征节点的环境特征，从而识别节点的拓扑等价性，以保证结构解析专家系统结构产生器的穷举和非冗余性。     

极紫外(EUV)光刻胶树脂及合成方法进展

极紫外光刻(EUVL)是实现22 nm及以下节点集成电路制造最为高效的工艺技术.本文介绍了 EUV光刻胶树脂的结构特点及其对光刻性能的影响,并着重整理了近年来化学增幅型和非化学增幅型EUV光刻胶树脂的合成方法.最后,对EUV光刻胶树脂的结构设计进行了分析与展望.     

计算机自动结构解析专家系统研究—一个新的拓扑等价性识别…

介绍了一个正确识别化合物结构片断中各原子的拓扑等价性问题的新算法，算法中引入了节点矩阵Ｎ和键矩阵Ｂ，并由这两个新矩阵来计算化合物结构图中的各节点的特征值，以此来表征节点的环境特征，从而识别节点的拓扑等价性，以保证结构解析专家系统结构产生器的穷举和非冗余性。     

串级质谱应用于蛋白质科学研究的实例介绍

串级质谱（MS/MS）分析是近年来迅猛发展的仪器分析技术,对于化学相关学科与其他交叉领域都起到了极大的推动作用。在使用计算机辅助解析后,常常需要进一步进行图谱指认,而这部分内容常常被化学教学所忽略。从串级质谱的原理出发,详细分析了肽段在二级质谱中可能的断裂模式与结构特性,并进一步以模型蛋白为例详细分析了串级质谱的图谱指认和峰的归属的基本原理与实用性方法,以期对教学与科研有助。     

化学结构正规编码及其信息处理功能

自从计算机在化学领域中应用以来,化学结构的正规编码已广泛用来作为一种化学文献的信息索引,它还在开发化学智能系统中发挥着重要作用。正规编码生成是以化学结构图的拓扑性质为基础的。本文首先概述编码的基本原理及其生成的方法,随后分析这种编码在当今文献数据库、化学反应数据库、分子设计以及合成反应路线规划等化学信息系统中的功能。     

运用Mercury软件辅助高中晶体结构教学

针对晶体结构教学的抽象性,运用Mercury软件辅助教学,该软件具有读取晶体信息文件、显示或编辑多种样式的晶体结构图等功能,能实现晶体配位数、晶体堆积方式、晶胞模型建构、晶胞微观结构分析等知识点的可视化教学,使教学有据可依,有助于发展学生宏观辨识与微观探析、证据推理与模型认知的化学学科核心素养,是信息技术与学科教学整合...     

基于Jaccard系数提高核磁共振波谱代谢物数据库匹配的精度

NMR代谢组学检测完成后,人们通常基于化学位移值在人类代谢组学数据库(human metabolome database,HMDB)上进行手动代谢物匹配,然而该方法对代谢物的鉴定较为粗糙,准确度不高。本研究试图基于建立一种更加合理,且能够自动寻峰并根据数据库匹配代谢物方法。通过分析HMDB的峰匹配方法,提出了基于Jaccard系数和匹配率(匹配的峰数目/总峰数)的新方法,基于MATLAB编程实现,然后比较HMDB中1D NMR search和本方法对于同一段随机化学位移列表的匹配结果。分析结果显示,对于同一随机化学位移列表,HMDB的匹配结果中排在前20位的物质峰数目超过16的占60%,说明其匹配方法偏向于峰数目较多的物质;HMDB用于峰匹配排序的评分与峰匹配率有明显区别,而本方法匹配评分与匹配率较为接近;且HMDB匹配结果排在第10位的物质与该随机序列没有可匹配的化学位移值。本文对于HMDB峰匹配算法存在的不足进行了改进,并发现基于Jaccard分数的匹配算法能够提高根据代谢物数据库进行NMR代谢物鉴定的精度。     

具有串/并联复合离子输运特性的仿生纳米通道的构筑与整流性能

人工构筑了基于分枝氧化铝纳米通道的串/并联复合的纳流体二极管体系, 其具有可调的离子整流性能. 在这种两级分枝结构的1-2-2, 1-2-3, 1-3-2和1-3-3型氧化铝纳米通道中, 若将每一个分枝节点等效为一个二极管, 那么其一级分枝节点相当于串联的1个二极管, 二级分枝节点相当于并联的多个二极管. 因此1-2-2和1-2-3型纳米通道的电路图可等效为并联的2个二极管与第3个二极管相串联, 1-3-2和1-3-3型纳米通道的电路图可等效为并联的3个二极管与第4个二极管相串联. 但由于1-2-2和1-2-3型以及1-3-2和1-3-3型的二级分枝的结构和数目不同, 可将这4种纳米通道等效为不同的串/并联复合特性的纳流体二极管体系, 并且表现出依次增大的离子整流. 即分枝氧化铝纳米通道内部一级分枝和二级分枝的结构或数目共同调控的表面电荷非对称性可以改变其离子整流性能. 进一步地, 具有代表性的1-2-2型分枝纳米通道的整流率随分枝通道长度的增加而增加, 这表明分枝部分对整个串/并联复合纳流体二极管的整流特性起到决定性的作用. 相比于以前的单个离子二极管体系, 这种具有串/并联复合特性的多级分枝氧化铝纳米通道将为构筑更复杂的仿生纳流体二极管的研究提供有价值的借鉴.     

复杂化学模式群的非线性映射及其应用

提出新的非线性映射算法，并分别采用传统非线性映射算法和新的非线性映射算法，将8维橄榄油样本映射于平面。其中新的非线性映射算法获得更好保留样本模式拓扑结构的映射平面，映射平面清晰地反映模式的类别关系，即同类模式都清晰地聚集在一起，实现聚类。     

化学指纹图谱的相似性测度及其评价方法

提出化学指纹图谱相似性测度概念,并以峰数弹性、峰比例同态性和峰面积同 态性为评价指标,用于多角度评价化学指纹图谱相似性测试优劣,根据这些评价指 标,用计算机仿真方法研究比较了6种相似性测度,结果表明夹角余弦测试用于度 量指纹图谱间谱峰比例的波动较适宜,峰匹配测度可较灵敏地检测小峰个数波动, 而欧氏距离测试则具有较好的综合评价能力。最后,采用实测的参麦注射液色谱分 析谱图。研究考察了上述计算机仿真实验结果,证明仿真结果符合实际情况,这表 明本研究方法可用于评价化学指纹图谱相似性测度。     

Efficient substructure searching of large chemical libraries: the ABCD chemical cartridge

Efficient substructure searching is a key requirement for any chemical information management system. In this paper, we describe the substructure search capabilities of ABCD, an integrated drug discovery informatics platform developed at Johnson & Johnson Pharmaceutical Research & Development, L.L.C. The solution consists of several algorithmic components: 1) a pattern mapping algorithm for solving the subgraph isomorphism problem, 2) an indexing scheme that enables very fast substructure searches on large structure files, 3) the incorporation of that indexing scheme into an Oracle cartridge to enable querying large relational databases through SQL, and 4) a cost estimation scheme that allows the Oracle cost-based optimizer to generate a good execution plan when a substructure search is combined with additional constraints in a single SQL query. The algorithm was tested on a public database comprising nearly 1 million molecules using 4,629 substructure queries, the vast majority of which were submitted by discovery scientists over the last 2.5 years of user acceptance testing of ABCD. 80.7% of these queries were completed in less than a second and 96.8% in less than ten seconds on a single CPU, while on eight processing cores these numbers increased to 93.2% and 99.7%, respectively. The slower queries involved extremely generic patterns that returned the entire database as screening hits and required extensive atom-by-atom verification.     

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: II. Case studies and applications

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman’s subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.     

Identification of family-specific residue packing motifs and their use for structure-based protein function prediction: I. Method development

Protein function prediction is one of the central problems in computational biology. We present a novel automated protein structure-based function prediction method using libraries of local residue packing patterns that are common to most proteins in a known functional family. Critical to this approach is the representation of a protein structure as a graph where residue vertices (residue name used as a vertex label) are connected by geometrical proximity edges. The approach employs two steps. First, it uses a fast subgraph mining algorithm to find all occurrences of family-specific labeled subgraphs for all well characterized protein structural and functional families. Second, it queries a new structure for occurrences of a set of motifs characteristic of a known family, using a graph index to speed up Ullman’s subgraph isomorphism algorithm. The confidence of function inference from structure depends on the number of family-specific motifs found in the query structure compared with their distribution in a large non-redundant database of proteins. This method can assign a new structure to a specific functional family in cases where sequence alignments, sequence patterns, structural superposition and active site templates fail to provide accurate annotation.     

Similarity searching in databases of three-dimensional molecules and macromolecules.

This paper discusses algorithmic techniques for measuring the degree of similarity between pairs of three-dimensional (3-D) chemical molecules represented by interatomic distance matrices. A comparison of four methods for the calculation of 3-D structural similarity suggests that the most effective one is a procedure that identifies pairs of atoms, one from each of the molecules that are being compared, that lie at the center of geometrically-related volumes of 3-D space. This atom mapping method enables the calculation of a wide range of types of intermolecular similarity coefficient, including measures that are based on physicochemical data. Massively-parallel implementations of the method are discussed, using the AMT Distributed Array Processor, that achieve a substantial increase in performance when compared with a sequential implementation on a UNIX workstation. Current work involves the use of angular information and the extension of the method to field-based similarity searching. Similarity searching in 3-D macromolecules is effected by the use of a maximal common subgraph (MCS) isomorphism algorithm with a novel, graph-based representation of the tertiary structures of proteins. This algorithm is being used to identify similarities between the 3-D structures of proteins in the Brookhaven Protein Data Bank; its use is exemplified by searches involving the NAD-binding fold motif.     

Step-by-step calculation of all maximum common substructures through a constraint satisfaction based algorithm

In this paper we propose a new algorithm for subgraph isomorphism based on the representation of molecular structures as colored graphs and the representation of these graphs as vectors in n-dimensional spaces. The presented process that obtains all maximum common substructures is based on the solution of a constraint satisfaction problem defined as the common m-dimensional space (m< or =n) in which the vectors representing the matched graphs can be defined.     

Searching for patterns of amino acids in 3D protein structures

This paper describes the program ASSAM, which has been developed to search for patterns of amino acid side-chains in the 3D structures in the Protein Data Bank. ASSAM represents an amino acid by a vector drawn from the main chain towards the functional part of the amino acid and then computes a graph representation of a protein in which the individual side-chain vectors are the nodes and the intervector distances are the edges. The presence of a query pattern in a Protein Data Bank structure can then be searched for by means of a subgraph isomorphism algorithm. Recent enhancements to ASSAM allow searches to include the following: the main-chain structure in addition to the side-chains; the secondary structure and solvent accessibility of side-chains; allowable distances from a known binding-site; disulfide bridges; and improved generic and wild-card queries. The effectiveness of these approaches is demonstrated by extensive searches of the Protein Data Bank for typical 3D query patterns.     

Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases

This paper reports an evaluation of both graph-based and fingerprint-based measures of structural similarity, when used for virtual screening of sets of 2D molecules drawn from the MDDR and ID Alert databases. The graph-based measures employ a new maximum common edge subgraph isomorphism algorithm, called RASCAL, with several similarity coefficients described previously for quantifying the similarity between pairs of graphs. The effectiveness of these graph-based searches is compared with that resulting from similarity searches using BCI, Daylight and Unity 2D fingerprints. Our results suggest that graph-based approaches provide an effective complement to existing fingerprint-based approaches to virtual screening.     

一种在无机晶体结构中检索特定子结构的计算机方法

提出了一种针对无机晶体化合物的子结构检索方法.该方法以VF2子图同构算法为基础,针对无机晶体化合物的结构特点,采用了两种策略以提高子结构检索的效率:(1)引入晶体的对称性信息避免了在等价原子间进行的大量重复性计算;(2)采用结构编码预筛选可以有效地减少目标结构的数量.我们以在无机微孔分子筛数据库中进行子结构检索为例测试该方法的有效性.测试结果表明,该方法可以快速且准确地在分子筛数据库中检索包含特定子结构的记录.两种检索策略的引入大大降低了子结构检索的复杂度,检索速度可提高3-5个数量级.该方法通过Perl语言实现,具有较好的可移植性.     

Substructure isomorphism matrix

A substructure isomorphism matrix n x p contains binary elements describing which of the given p query structures (substructures) are part of the given n target structures (molecular structures). Such a matrix can be used to investigate the diversity of the target structures and allows the characterization and comparison of structural libraries. A quadratic substructure isomorphism matrix n x n is obtained if the same structures are used as molecular structures and as substructures; this matrix contains full information about the topological hierarchy of the n structures. A hierarchical arrangement of chemical structures is useful for the evaluation of results obtained from searches in structure databases.     

Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm

Recently a method (RASCAL) for determining graph similarity using a maximum common edge subgraph algorithm has been proposed which has proven to be very efficient when used to calculate the relative similarity of chemical structures represented as graphs. This paper describes heuristics which simplify a RASCAL similarity calculation by taking advantage of certain properties specific to chemical graph representations of molecular structure. These heuristics are shown experimentally to increase the efficiency of the algorithm, especially at more distant values of chemical graph similarity.