A frequent problem in computational modeling is the interconversion of chemical structures between different formats. While standard interchange formats exist (for example, Chemical Markup Language) and de facto standards have arisen (for example, SMILES format), the need to interconvert formats is a continuing problem due to the multitude of different application areas for chemistry data, differences in the data stored by different formats (0D versus 3D, for example), and competition between software along with a lack of vendor-neutral formats. We discuss, for the first time, Open Babel, an open-source chemical toolbox that speaks the many languages of chemical data. Open Babel version 2.3 interconverts over 110 formats. The need to represent such a wide variety of chemical and molecular data requires a library that implements a wide range of cheminformatics algorithms, from partial charge assignment and aromaticity detection, to bond order perception and canonicalization. We detail the implementation of Open Babel, describe key advances in the 2.3 release, and outline a variety of uses both in terms of software products and scientific research, including applications far beyond simple format interconversion. Open Babel presents a solution to the proliferation of multiple chemical file formats. In addition, it provides a variety of useful utilities from conformer searching and 2D depiction, to filtering, batch conversion, and substructure and similarity searching. For developers, it can be used as a programming library to handle chemical data in areas such as organic chemistry, drug design, materials science, and computational chemistry. It is freely available under an open-source license from http://openbabel.org
 .

/pdf/open-babel-an-open-chemical-toolbox-2c5goscrz3.pdf

Open Babel: An open chemical toolbox

Motivation: The ability to accurately measure structural similarities among small molecules is important for many analysis routines in drug discovery and chemical genomics. Algorithms used for this purpose include fragment-based fingerprint and graph-based maximum common substructure (MCS) methods. MCS approaches provide one of the most accurate similarity measures. However, their rigid matching policies limit them to the identification of perfect MCSs. To eliminate this restriction, we introduce a new mismatch tolerant search method for identifying flexible MCSs (FMCSs) containing a user-definable number of atom and/or bond mismatches. Results :T hefmcsR package provides an R interface, with the timeconsuming steps of the FMCS algorithm implemented in Cþþ .I t includes utilities for pairwise compound comparisons, structure similarity searching, clustering and visualization of MCSs. In comparison with an existing MCS tool, fmcsR shows better time performance over a wide range of compound sizes. When mismatching of atoms or bonds is turned on, the compute times increase as expected, and the resulting FMCSs are often substantially larger than their strict MCS counterparts. Based on extensive virtual screening (VS) tests, the flexible matching feature enhances the enrichment of active structures at the top of MCS-based similarity search results. With respect to overall and early enrichment performance, FMCS outperforms most of the seven other VS methods considered in these tests. Availability: fmcsR is freely available for all common operating systems from the Bioconductor site (http://www.bioconductor.org/

fmcsR: mismatch tolerant maximum common substructure searching in R

The emergence of Web databases has introduced new challenges related to their organization, access, integration, and interoperability. New approaches and techniques are needed to provide across-the-board transparency for accessing and manipulating Web databases irrespective of their data models, platforms, locations, or systems. In meeting these needs, it is necessary to build a middleware infrastructure to support flexible tools for information space organiza­tion, communication facilities, information discovery, content description, and assembly of data from heterogeneous sources. In this paper, we describe a scalable middleware for efficient data and application access that we have built using the available technologies. The resulting system is called WebFINDIT. It is a scalable and uniform infrastructure for locating and accessing heterogeneous and autonomous databases and applications.

A Scalable Middleware for Web Databases

Process-Oriented Case-Based Reasoning (PO-CBR) systems often use structured cases, which in turn require effective structure-based retrieval methods, especially when dealing with large processes and/or large case bases. Good retrieval performance can be facilitated by two-phased retrieval methods which first winnow candidate cases with a comparatively inexpensive retrieval phase, and then apply a more expensive strategy to rank the selected cases. Examples of such processes have been shown to provide good retrieval results in limited retrieval time. However, studies of such methods have focused primarily on overall performance, rather than on how the individual contributions of each phase interact to affect overall performance. This misses an opportunity to tune the component algorithms in light of the system task and case base characteristics, for specific task needs. This chapter examines two-phased retrieval as a means of addressing the complexity in many PO-CBR domains, and specifically examines the performance of each phase of two-phased retrieval individually, demonstrating characteristics of the phases’ interaction and providing general lessons for how to design and deploy two-phased retrieval systems.

A Study of Two-Phase Retrieval for Process-Oriented Case-Based Reasoning

Many real-world applications, such as bioinformatics, data mining, pattern recognition, and social network analysis, benefit from efficient solutions for the graph similarity search problem. Existing methods have limited scalability when they handle the large graph databases, for example, those with millions or billions of graphs that cannot fit in main memory. In this paper, we study the problem of graph similarity search under the graph edit distance constraint in external memory. We present an efficient framework for arbitrary $q$ - ${gram}$ -based representations of a graph. Specifically, we propose a $q$ - gram matrix index stored in hybrid layout in external memory to achieve efficient query processing, by converting the $q$ - gram counting filter into a sparse matrix-vector multiplication problem. Furthermore, we also boost the query performance by transforming the global filter to a 2-D query rectangle, which allows us to perform a query in a reduced region, significantly reducing the number of query I/Os in practice. Extensive experiments on real data sets confirm that 1) our method can compete with the state-of-the-art in-memory methods in index size and filtering ability, and outperform them on scalability of coping with the PubChem data set including 25 million chemical structure graphs and 2) compared with the popular $q$ - gram -based external inverted index, our external index structure needs much fewer number of query I/Os on the PubChem data set.

Efficient Graph Similarity Search in External Memory

Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and similarity search. With the fast accumulation of graph databases, similarity search in graph databases has emerged as an important research topic. Graph similarity search has applications in a wide range of domains including cheminformatics, bioinformatics, sensor network management, social network management, and XML documents, among others.Most of the current graph indexing methods focus on subgraph query processing, i.e. determining the set of database graphs that contains the query graph and hence do not directly support similarity search. In data mining and machine learning, various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models for supervised learning, graph kernel functions have (i) high computational complexity and (ii) non-trivial difficulty to be indexed in a graph database.Our objective is to bridge graph kernel function and similarity search in graph databases by proposing (i) a novel kernel-based similarity measurement and (ii) an efficient indexing structure for graph data management. Our method of similarity measurement builds upon local features extracted from each node and their neighboring nodes in graphs. A hash table is utilized to support efficient storage and fast search of the extracted local features. Using the hash table, a graph kernel function is defined to capture the intrinsic similarity of graphs and for fast similarity query processing. We have implemented our method, which we have named G-hash, and have demonstrated its utility on large chemical graph databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Most importantly, the new similarity measurement and the index structure is scalable to large database with smaller indexing size, faster indexing construction time, and faster query processing time as compared to state-of-the-art indexing methods such as C-tree, gIndex, and GraphGrep.

/pdf/g-hash-towards-fast-kernel-based-similarity-search-in-large-2lq59ieawn.pdf

G-hash: towards fast kernel-based similarity search in large graph databases

Similaritysearch in chemical structure databases is an important problem with many applications in chemical genomics, drug design, and efficient chemical probe screening among others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases. To bridge graph kernel function and similarity search in chemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, named G-hash, to large chemical databases. Our results show that the G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep. Efficient similarity query processing method for large chemical databases is challenging since we need to balance running time efficiency and similarity search accuracy. Our previous similarity search method, G-hash, provides a new way to perform similarity search in chemical databases. Experimental study validates the utility of G-hash in chemical databases.

/pdf/application-of-kernel-functions-for-accurate-similarity-1mdrom5k2z.pdf

Application of kernel functions for accurate similarity search in large chemical databases

Similarity search in chemical structure databases is an important problem with many applications in chemicalgenomics, drug design, and efficient chemical probe screeningamong others. It is widely believed that structure based methods provide an efficient way to do the query. Recently various graph kernel functions have been designed to capture the intrinsic similarity of graphs. Though successful in constructing accurate predictive and classification models, graph kernel functions can not be applied to large chemical compound database due to the high computational complexity and the difficulties in indexing similarity search for large databases.To bridge graph kernel function and similarity search inchemical databases, we applied a novel kernel-based similarity measurement, developed in our team, to measure similarity of graph represented chemicals. In our method, we utilize a hash table to support new graph kernel function definition, efficient storage and fast search. We have applied our method, namedG-hash, to large chemical databases. Our results show thatthe G-hash method achieves state-of-the-art performance for k-nearest neighbor (k-NN) classification. Moreover, the similarity measurement and the index structure is scalable to large chemical databases with smaller indexing size, and faster query processing time as compared to state-of-the-art indexing methods such as Daylight fingerprints, C-tree and GraphGrep.

/pdf/application-of-kernel-functions-for-accurate-similarity-1veghcb936.pdf

Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases.

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G-hash: towards fast kernel-based similarity search in large graph databases

Application of kernel functions for accurate similarity search in large chemical databases

Application of Kernel Functions for Accurate Similarity Search in Large Chemical Databases

G-Hash: Towards Fast Kernel-Based Similarity Search in Large Graph Databases.