Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work

A survey of chemical information systems

A survey of the features, functions, and characteristics of a fairly wide variety of chemical information storage and retrieval systems currently in operation is given. The types of systems (together with an identification of the specific systems) addressed within this survey are as follows: patents and bibliographies (Derwent's Patent System; IFI Comprehensive Database; PULSAR); pharmacology and toxicology (Chemfile; PAGODE; CBF; HEEDA; NAPRALERT; MAACS); the chemical information system (CAS Chemical Registry System; SANSS; MSSS; CSEARCH; GINA; NMRLIT; CRYST; XTAL; PDSM; CAISF; RTECS Search System; AQUATOX; WDROP; OHMTADS; MLAB; Chemlab); spectra (OCETH; ASTM); crystals (CRYSRC); and physical properties (DETHERM). Summary characteristics and current trends in chemical information systems development are also examined

IMPROVING MOLECULAR FINGERPRINT SIMILARITY VIA ENHANCED FOLDING

Drug discovery depends on scientists finding similarity in molecular fingerprints to the drug target. A new way to improve the accuracy of molecular fingerprint folding is presented. The goal is to alleviate a growing challenge due to excessively long fingerprints. This improved method generates a new shorter fingerprint that is more accurate than the basic folded fingerprint. Information gathered during preprocessing is used to determine an optimal attribute order. The most commonly used blocks of bits can then be organized and used to generate a new improved fingerprint for more optimal folding. We thenapply the widely usedTanimoto similarity search algorithm to benchmark our results. We show an improvement in the final results using this method to generate an improved fingerprint when compared against other traditional folding methods

Evaluation of a Bayesian inference network for ligand-based virtual screening

Background Bayesian inference networks enable the computation of the probability that an event will occur. They have been used previously to rank textual documents in order of decreasing relevance to a user-defined query. Here, we modify the approach to enable a Bayesian inference network to be used for chemical similarity searching, where a database is ranked in order of decreasing probability of bioactivity. Results Bayesian inference networks were implemented using two different types of network and four different types of belief function. Experiments with the MDDR and WOMBAT databases show that a Bayesian inference network can be used to provide effective ligand-based screening, especially when the active molecules being sought have a high degree of structural homogeneity; in such cases, the network substantially out-performs a conventional, Tanimoto-based similarity searching system. However, the effectiveness of the network is much less when structurally heterogeneous sets of actives are being sought. Conclusion A Bayesian inference network provides an interesting alternative to existing tools for ligand-based virtual screening

The study of probability model for compound similarity searching

Information Retrieval or IR system main task is to retrieve relevant documents according to the users query. One of IR most popular retrieval model is the Vector Space Model. This model assumes relevance based on similarity, which is defined as the distance between query and document in the concept space. All currently existing chemical compound database systems have adapt the vector space model to calculate the similarity of a database entry to a query compound. However, it assumes that fragments represented by the bits are independent of one another, which is not necessarily true. Hence, the possibility of applying another IR model is explored, which is the Probabilistic Model, for chemical compound searching. This model estimates the probabilities of a chemical structure to have the same bioactivity as a target compound. It is envisioned that by ranking chemical structures in decreasing order of their probability of relevance to the query structure, the effectiveness of a molecular similarity searching system can be increased. Both fragment dependencies and independencies assumption are taken into consideration in achieving improvement towards compound similarity searching system. After conducting a series of simulated similarity searching, it is concluded that PM approaches really did perform better than the existing similarity searching. It gave better result in all evaluation criteria to confirm this statement. In terms of which probability model performs better, the BD model shown improvement over the BIR model

Design of a Structure Search Engine for Chemical Compound Database

The search for structural fragments (substructures) of compounds is very important in medicinal chemistry, QSAR, spectroscopy, and many other fields. In the last decade, with the development of hardware and evolution of database technologies, more and more chemical compound database applications have been developed along with interfaces of searching for targets based on user input. Due to the algorithmic complexity of structure comparison, essentially a graph isomorphism problem, the current applications mainly work by the approximation of the comparison problem based on certain chemical perceptions and their search interfaces are often e-mail based. The procedure of approximation usually invokes subjective assumption. Therefore, the accuracy of the search is undermined, which may not be acceptable for researchers because in a time-consuming drug design, accuracy is always the first priority. In this dissertation, a design of a search engine for chemical compound database is presented.The design focuses on providing a solution to develop an accurate and fast search engine without sacrificing performance. The solution is comprehensive in a way that a series of related problems were addressed throughout the dissertation with proposed methods. Based on the design, a flexible computing model working for compound search engine can be established and the model can be easily applied to other applications as well. To verify the solution in a practical manner, an implementation based on the presented solution was developed. The implementation clarifies the coupling between theoretic design and technique development. In addition, a workable implementation can be deployed to test the efficiency and effectiveness of the design under variant of experimental data

Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics.

Novel metabolites distinct from canonical pathways can be identified through the integration of three cheminformatics tools: BinVestigate, which queries the BinBase gas chromatography-mass spectrometry (GC-MS) metabolome database to match unknowns with biological metadata across over 110,000 samples; MS-DIAL 2.0, a software tool for chromatographic deconvolution of high-resolution GC-MS or liquid chromatography-mass spectrometry (LC-MS); and MS-FINDER 2.0, a structure-elucidation program that uses a combination of 14 metabolome databases in addition to an enzyme promiscuity library. We showcase our workflow by annotating N-methyl-uridine monophosphate (UMP), lysomonogalactosyl-monopalmitin, N-methylalanine, and two propofol derivatives

Jet Substructure Studies with CMS Open Data

We use public data from the CMS experiment to study the 2-prong substructure of jets. The CMS Open Data is based on 31.8/pb of 7 TeV proton-proton collisions recorded at the Large Hadron Collider in 2010, yielding a sample of 768,687 events containing a high-quality central jet with transverse momentum larger than 85 GeV. Using CMS's particle flow reconstruction algorithm to obtain jet constituents, we extract the 2-prong substructure of the leading jet using soft drop declustering. We find good agreement between results obtained from the CMS Open Data and those obtained from parton shower generators, and we also compare to analytic jet substructure calculations performed to modified leading-logarithmic accuracy. Although the 2010 CMS Open Data does not include simulated data to help estimate systematic uncertainties, we use track-only observables to validate these substructure studies.Comment: 35 pages, 19 figures, 6 tables, source contains sample event and additional plots; v2: references updated and figure formatting improved; v3: approximate version to appear in PR