This paper presents a method for finding patterns in three dimensional (3D) graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining.

Computer Chemistry is of quickly increasing importance, in particular since the flood of data is rapidly growing with the introduction of Combinatorial Chemistry using methods of synthesis in large quantities (libraries) and high throughput screening. The accompanying software that allows to optimize such experiments in advance and to economize the cost of measurement afterwards uses in particular mathematical models of molecules and their description. Such models will be described here and the present state of the generation of molecular models in the computer will be discussed, from the mathematical point of view. A brief description of several applications is given. 1

MOLGEN is a computer program system which is designed for generating molecular graphs fast, redundancy free and exhaustively. In the present paper we describe its basic features, new features of the current release MOLGEN 3.5, and future developments which provide considerable improvements and extensions. 1 Introduction MOLGEN [1--7] is a generator for molecular graphs (=connectivity isomers or constitutional formulae) allowing to generate all isomers that correspond to a given molecular formula and (optional) further conditions like prescribed and forbidden substructures, ring sizes etc. The input consists of ffl the empirical formula, together with ffl an optional list of macroatoms, which means prescribed substructures that must not overlap, ffl an optional goodlist, that consists of prescribed substructures which may overlap, ffl an optional badlist, containing forbidden substructures, ffl an optional interval for the minimal and maximal size of rings, ffl an optional num...

One of the growing challenges in life science research lies in finding useful, descriptive or quantitative data about newly reported biomolecules (genes, proteins, metabolites and drugs). An even greater challenge is finding information that connects these genes, proteins, drugs or metabolites to each other. Much of this information is scattered through hundreds of different databases, abstracts or books and almost none of it is particularly well integrated. While some efforts are being undertaken at the NCBI and EBI to integrate many different databases together, this still falls short of the goal of having some kind of humanreadable synopsis that summarizes the state of knowledge about a given biomolecule – especially small molecules. To address this shortfall, we have developed BioSpider. BioSpider is essentially an automated report generator designed specifically to tabulate and summarize data on biomolecules – both large and small. Specifically, BioSpider allows users to type in almost any kind of biological or chemical identifier (protein/gene name, sequence, accession number, chemical name, brand name, SMILES string, InCHI string, CAS number, etc.) and it returns an in-depth synoptic report (~3-30 pages in length) about that biomolecule and any other biomolecule it may target. This summary includes physico-chemical parameters, images, models, data files, descriptions and predictions concerning the query molecule. BioSpider uses a web-crawler to scan through dozens of public databases and employs a variety of specially developed text mining tools and locally developed prediction tools to find, extract and assemble data for its reports. Because of its breadth, depth and comprehensiveness, we believe BioSpider will prove to be a particularly valuable tool for researchers in metabolomics. BioSpider is available at: www.biospider.ca 1.

In this paper a software package is described that allows using the MM2 force field to compute the energy of three-dimensional conformations of molecules; this energy is minimized, the resulting structures are automatically classified. Thus we get an impression about the set of all low-energy three-dimensional conformations of a given molecule defined in terms of two dimensional connectivity information. The accuracy of the resulting information can be tuned by changing input parameters for the method presented. This is a hybrid which is built from a method for classification of three-dimensional molecules, from a conjugate gradient method for local minimization and from operators stemming from evolutionary algorithms. The latter have proven successful in the solution of di#cult optimization tasks (not only) in mathematical chemistry. They are of special importance for the approximate solution of problems where global optima of multimodal functions in high dimensional spaces are sought. and in Computer Chemistry - match, no. 38, October 1998, pp. 137--159. # This work was supported by the DFG under grant Ke 201/16-1. ## e-mail: clemens.frey@uni-bayreuth.de The paper is organized in three sections. The first one gives a formalization of the problem and shows in which way this problem was solved up to now. In the second section the evolutionary algorithm is introduced after a short presentation of a general formalization for this kind of algorithms. Each operator of our method is shown in a separate subsection. A definition of the function which determines the transition from one generation to the next generation concludes this section. The last section shows trials and corresponding results obtained with the presented method. The summary of trials is followed by list...

The advent of Grid Computing promises users the power to have access to a vast amount of heterogeneous, distributed resources. The envisaged goal is to enable users and applications to seamlessly access these resources to solve complex large-scale problems whether in science, engineering, or commerce. To realize this goal the numerous barriers that normally separate different computing systems within and across organizations must be addressed. This can be achieved through effective standardization. Standardization plays a crucial role in achieving interoperability, portability and reusability of components and systems. The Open Grid Services Infrastructure (OGSI) Specification defines a set of conventions and extensions that contribute to the standardization of Grid Services. Since Grid Services are typically implemented as Web Services, they rely heavily on XML technologies. Thus, the focus of this research is to propose a multilaterally-interoperable Grid Service Framework that is robust and secure, using common XML standards relating to SOAP and Web Services. (After only four months into the research, only an analysis of the problem context is provided).

In this paper we describe a new algorithm for multiple semi-flexible superpositioning of drug-sized molecules. The algorithm identifies structural similarities of two or more molecules. When comparing a set of molecules on the basis of their three-dimensional structures, one is faced with two main problems. (1) Molecular structures are not fixed but flexible, i.e., a molecule adopts different forms. To address this problem, we consider a set of conformers per molecule. As conformers we use representatives of conformational ensembles, generated by the program ZIBMol. (2) The degree of similarity may vary considerably among the molecules. This problem is addressed by searching for similar substructures present in arbitrary subsets of the given set of molecules. The algorithm requires to preselect a reference molecule. All molecules are compared to this reference molecule. For this pairwise comparison we use a two-step approach. Clique detection on the correspondence graph of the molecular structures is used to generate start transformations, which are then iteratively improved to compute large common substructures. The results of the pairwise comparisons are efficiently merged using binary matching trees. All common substructures that were found, whether they are common to all or only a few molecules, are ranked according to different criteria, such as number of molecules containing the substructure, size of substructure, and geometric fit. For evaluating the geometric fit, we extend a known scoring function by introducing weights which allow to favor potential pharmacophore points. Despite considering the full atomic information for identifying multiple structural similarities, our algorithm is quite fast. Thus it is well suited as an interactive tool for the exploration of structural similarities of drugsized molecules.

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In silico screening methods based on the 3D structures of the ligands or of the proteins have become an essential tool to facilitate the drug discovery process. To achieve such process, the 3D structures of the small chemical compounds have to be generated. In addition, for ligand-based screening computations or hierarchical structurebased screening projects involving a rigid-body docking step, it is necessary to generate multiconformer 3D models for each input ligand to increase the efficiency of the search. However, most academic or commercial compound collections are delivered in 1D SMILES (simplified molecular input line entry system) format or in 2D SDF (structure data file), highlighting the need for free 1D/2D to 3D structure generators. Frog is an on-line service aimed at generating 3D conformations for drug-like compounds starting from their 1D or 2D descriptions. Given the atomic constitution of the molecules and connectivity information, Frog can identify the different unambiguous isomers corresponding to each compound, and generate single or multiple low-to-medium energy 3D conformations, using an assembly process that does not presently consider ring flexibility. Tests show that Frog is able to generate bioactive conformations close to those observed in crystallographic complexes. Frog can be accessed at

preparation influences the quality of QSPR models?