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Semantic Web Integration of Cheminformatics Resources with the SADI Framework Leonid L Chepelev1* and Michel Dumontier1,2,3
Chepelev and Dumontier Journal of Cheminformatics 2011, 3:16 http://www.jcheminf.com/content/3/1/16 METHODOLOGY Open Access Semantic Web integration of Cheminformatics resources with the SADI framework Leonid L Chepelev1* and Michel Dumontier1,2,3 Abstract Background: The diversity and the largely independent nature of chemical research efforts over the past half century are, most likely, the major contributors to the current poor state of chemical computational resource and database interoperability. While open software for chemical format interconversion and database entry cross-linking have partially addressed database interoperability, computational resource integration is hindered by the great diversity of software interfaces, languages, access methods, and platforms, among others. This has, in turn, translated into limited reproducibility of computational experiments and the need for application-specific computational workflow construction and semi-automated enactment by human experts, especially where emerging interdisciplinary fields, such as systems chemistry, are pursued. Fortunately, the advent of the Semantic Web, and the very recent introduction of RESTful Semantic Web Services (SWS) may present an opportunity to integrate all of the existing computational and database resources in chemistry into a machine-understandable, unified system that draws on the entirety of the Semantic Web. Results: We have created a prototype framework of Semantic Automated Discovery and Integration (SADI) framework SWS that exposes the QSAR descriptor functionality of the Chemistry Development Kit. Since each of these services has formal ontology-defined input and output classes, and each service consumes and produces RDF graphs, clients can automatically reason about the services and available reference information necessary to complete a given overall computational task specified through a simple SPARQL query. -
Glossary of Terms Used in Photochemistry, 3Rd Edition (IUPAC
Pure Appl. Chem., Vol. 79, No. 3, pp. 293–465, 2007. doi:10.1351/pac200779030293 © 2007 IUPAC INTERNATIONAL UNION OF PURE AND APPLIED CHEMISTRY ORGANIC AND BIOMOLECULAR CHEMISTRY DIVISION* SUBCOMMITTEE ON PHOTOCHEMISTRY GLOSSARY OF TERMS USED IN PHOTOCHEMISTRY 3rd EDITION (IUPAC Recommendations 2006) Prepared for publication by S. E. BRASLAVSKY‡ Max-Planck-Institut für Bioanorganische Chemie, Postfach 10 13 65, 45413 Mülheim an der Ruhr, Germany *Membership of the Organic and Biomolecular Chemistry Division Committee during the preparation of this re- port (2003–2006) was as follows: President: T. T. Tidwell (1998–2003), M. Isobe (2002–2005); Vice President: D. StC. Black (1996–2003), V. T. Ivanov (1996–2005); Secretary: G. M. Blackburn (2002–2005); Past President: T. Norin (1996–2003), T. T. Tidwell (1998–2005) (initial date indicates first time elected as Division member). The list of the other Division members can be found in <http://www.iupac.org/divisions/III/members.html>. Membership of the Subcommittee on Photochemistry (2003–2005) was as follows: S. E. Braslavsky (Germany, Chairperson), A. U. Acuña (Spain), T. D. Z. Atvars (Brazil), C. Bohne (Canada), R. Bonneau (France), A. M. Braun (Germany), A. Chibisov (Russia), K. Ghiggino (Australia), A. Kutateladze (USA), H. Lemmetyinen (Finland), M. Litter (Argentina), H. Miyasaka (Japan), M. Olivucci (Italy), D. Phillips (UK), R. O. Rahn (USA), E. San Román (Argentina), N. Serpone (Canada), M. Terazima (Japan). Contributors to the 3rd edition were: A. U. Acuña, W. Adam, F. Amat, D. Armesto, T. D. Z. Atvars, A. Bard, E. Bill, L. O. Björn, C. Bohne, J. Bolton, R. Bonneau, H. -
Joelib Tutorial
JOELib Tutorial A Java based cheminformatics/computational chemistry package Dipl. Chem. Jörg K. Wegner JOELib Tutorial: A Java based cheminformatics/computational chemistry package by Dipl. Chem. Jörg K. Wegner Published $Date: 2004/03/16 09:16:14 $ Copyright © 2002, 2003, 2004 Dept. Computer Architecture, University of Tübingen, GermanyJörg K. Wegner Updated $Date: 2004/03/16 09:16:14 $ License This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation version 2 of the License. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Documents PS (JOELibTutorial.ps), PDF (JOELibTutorial.pdf), RTF (JOELibTutorial.rtf) versions of this tutorial are available. Plucker E-Book (http://www.plkr.org) versions: HiRes-color (JOELib-HiRes-color.pdb), HiRes-grayscale (JOELib-HiRes-grayscale.pdb) (recommended), HiRes-black/white (JOELib-HiRes-bw.pdb), color (JOELib-color.pdb), grayscale (JOELib-grayscale.pdb), black/white (JOELib-bw.pdb) Revision History Revision $Revision: 1.5 $ $Date: 2004/03/16 09:16:14 $ $Id: JOELibTutorial.sgml,v 1.5 2004/03/16 09:16:14 wegner Exp $ Table of Contents Preface ........................................................................................................................................................i 1. Installing JOELib -
Cheminformatics and Chemical Information
Cheminformatics and Chemical Information Matt Sundling Advisor: Prof. Curt Breneman Department of Chemistry and Chemical Biology/Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Rensselaer Exploratory Center for Cheminformatics Research http://reccr.chem.rpi.edu/ Many thanks to Theresa Hepburn! Upcoming lecture plug: Prof. Curt Breneman DSES Department November 15th Advances in Cheminformatics: Applications in Biotechnology, Drug Design and Bioseparations Cheminformatics is about collecting, storing, and analyzing [usually large amounts of] chemical data. •Pharmaceutical research •Materials design •Computational/Automated techniques for analysis •Virtual high-throughput screening (VHTS) QSAR - quantitative structure-activity relationship Molecular Model Activity Structures O N N Cl AAACCTCATAGGAAGCA TACCAGGAATTACATCA … Molecular Model Activity Structures O N N Cl AAACCTCATAGGAAGCATACCA GGAATTACATCA… Structural Descriptors Physiochemical Descriptors Topological Descriptors Geometrical Descriptors Molecular Descriptors Model Activity Structures Acitivty: bioactivity, ADME/Tox evaluation, hERG channel effects, p-456 isozyme inhibition, anti- malarial efficacy, etc… Structural Descriptors Physiochemical Descriptors Topological Descriptors Molecule D1 D2 … Activity (IC50) molecule #1 21 0.1 Geometrical Descriptors = molecule #2 33 2.1 molecule #3 10 0.9 + … Activity Molecular Descriptors Model Activity Structures Goal: Minimize the error: ∑((yi −f xi)) f1(x) f2(x) Regression Models: linear, multi-linear, -
Committee on Publications and Cheminformatics Data Standards (CPCDS)
Committee on Publications and Cheminformatics Data Standards (CPCDS) Report to the Bureau, April 2020 Leah McEwen, Committee Chair I. Executive Summary The IUPAC Committee on Publications and Cheminformatics Data Standards (CPCDS) advises on issues related to dissemination of information, primarily of IUPAC outputs. The portfolio is expanding and diversifying, in types of content, modes of publication and potential readership. Publication of IUPAC recommendations, technical reports and other information resources remains at the core of IUPAC dissemination activity and that of CPCDS. Increasingly the committee also focuses on the development and dissemination of chemical data and information standards to facilitate robust communication in the digital environment. Further details on these two priority areas are provided in Appendix A (Subcommittee on Publications: Report to CPCDS) and Appendix B (CPCDS Chair’s Statement: Towards a “Digital IUPAC”). The 2018-2019 biennium brought stability to key systems and workflows including those supporting IUPAC’s flagship journal Pure and Applied Chemistry (PAC) and online access to the Compendium of Chemical Terminology (a.k.a., the “Gold Book”). A number of collaborative symposia and workshops with strategic partners including CODATA and the GO FAIR Chemistry Implementation Network (ChIN) surfaced use cases and infrastructure needs for digital science among diverse community stakeholders. These activities lay the groundwork for developing a robust and systematic program for Digital IUPAC and “the creation of a consistent and interoperable global framework for human and machine-readable chemical information,” as articulated in the CPCDS Terms of Reference. This vision will be a critical component of success for IUPAC’s contribution towards the United Nations Sustainable Development Goals. -
Scalable Analysis of Large Datasets in Life Sciences
Scalable Analysis of Large Datasets in Life Sciences LAEEQ AHMED Doctoral Thesis in Computer Science Stockholm, Sweden 2019 Division of Computational Science and Technology School of Electrical Engineerning and Computer Science KTH Royal Institute of Technology TRITA-EECS-AVL-2019:69 SE-100 44 Stockholm ISBN: 978-91-7873-309-5 SWEDEN Akademisk avhandling som med tillstånd av Kungl Tekniska högskolan framlägges till offentlig granskning för avläggande av teknologie doktorsexamen i elektrotek- nik och datavetenskap tisdag den 3:e Dec 2019, klockan 10.00 i sal Kollegiesalen, Brinellvägen 8, KTH-huset, Kungliga Tekniska Högskolan. © Laeeq Ahmed, 2019 Tryck: Universitetsservice US AB To my respected parents, my beloved wife and my lovely brother and sister Abstract We are experiencing a deluge of data in all fields of scientific and business research, particularly in the life sciences, due to the development of better instrumentation and the rapid advancements that have occurred in informa- tion technology in recent times. There are major challenges when it comes to handling such large amounts of data. These range from the practicalities of managing these large volumes of data, to understanding the meaning and practical implications of the data. In this thesis, I present parallel methods to efficiently manage, process, analyse and visualize large sets of data from several life sciences fields at a rapid rate, while building and utilizing various machine learning techniques in a novel way. Most of the work is centred on applying the latest Big Data Analytics frameworks for creating efficient virtual screening strategies while working with large datasets. Virtual screening is a method in cheminformatics used for Drug discovery by searching large libraries of molecule structures. -
Cheminformatics for Genome-Scale Metabolic Reconstructions
CHEMINFORMATICS FOR GENOME-SCALE METABOLIC RECONSTRUCTIONS John W. May European Molecular Biology Laboratory European Bioinformatics Institute University of Cambridge Homerton College A thesis submitted for the degree of Doctor of Philosophy June 2014 Declaration This thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. This dissertation is not substantially the same as any I have submitted for a degree, diploma or other qualification at any other university, and no part has already been, or is currently being submitted for any degree, diploma or other qualification. This dissertation does not exceed the specified length limit of 60,000 words as defined by the Biology Degree Committee. This dissertation has been typeset using LATEX in 11 pt Palatino, one and half spaced, according to the specifications defined by the Board of Graduate Studies and the Biology Degree Committee. June 2014 John W. May to Róisín Acknowledgements This work was carried out in the Cheminformatics and Metabolism Group at the European Bioinformatics Institute (EMBL-EBI). The project was fund- ed by Unilever, the Biotechnology and Biological Sciences Research Coun- cil [BB/I532153/1], and the European Molecular Biology Laboratory. I would like to thank my supervisor, Christoph Steinbeck for his guidance and providing intellectual freedom. I am also thankful to each member of my thesis advisory committee: Gordon James, Julio Saez-Rodriguez, Kiran Patil, and Gos Micklem who gave their time, advice, and guidance. I am thankful to all members of the Cheminformatics and Metabolism Group. -
Print Special Issue Flyer
IMPACT FACTOR 4.411 an Open Access Journal by MDPI Cheminformatics, Past, Present, and Future: From Chemistry to Nanotechnology and Complex Systems Guest Editors: Message from the Guest Editors Prof. Dr. Humbert González- Cheminformatics techniques form have been interesting Díaz tools in the effort to explore large chemical spaces, [email protected] reducing at the same time animal testing as well as costs in Dr. Aliuska Duardo-Sanchez terms of materials and human resources on drug discovery [email protected] processes in medicinal chemistry. In addition, with the emergence of nanotechnologies, cheminformatics needs Prof. Dr. Alejandro Pazos to deal with drug–nanoparticle systems used as drug Sierra [email protected] delivery systems, drug co-therapy systems, etc. This brings to mind the application of cheminformatics in areas beyond drug discovery such as the fuel industry, polymer sciences, materials science, and biomedical engineering. Deadline for manuscript submissions: In this context, we propose to open this new issue to closed (15 November 2020) discuss with all colleagues worldwide all the past, present, and future challenges of cheminformatics. The present Special Issue is also associated with MOL2NET-05, the International Conference on Multidisciplinary Sciences, ISSN: 2624-5078, MDPI SciForum, Basel, Switzerland, 2019. The link of the conference: https://mol2net- 05.sciforum.net/. We especially encourage submissions of papers from colleagues worldwide to the conference (short communications) and complete versions (full papers) to the present Special Issue. mdpi.com/si/31770 SpeciaIslsue IMPACT FACTOR 4.411 an Open Access Journal by MDPI Editor-in-Chief Message from the Editor-in-Chief Prof. -
(12) United States Patent (10) Patent No.: US 8,716,300 B2 Piccariello Et Al
USOO871 6300B2 (12) United States Patent (10) Patent No.: US 8,716,300 B2 Piccariello et al. (45) Date of Patent: *May 6, 2014 (54) FREQUENCY MODULATED DRUG FOREIGN PATENT DOCUMENTS DELIVERY (FMDD) EP O914826 A1 5, 1999 (75) Inventors: Thomas Piccariello, Blacksburg, VA WO WOO3,O74474 A2 9, 2003 (US); Scott B. Palmer, Wilmette, IL OTHER PUBLICATIONS (US); John D. Price, Blacksburg, VA (US); Robert Oberlender, Blacksburg, Ama et al., Bull. Chem. Soc. Jpn., vol. 62, pp. 3464-3468 (1989). VA (US); Mary C. Spencer, Blacksburg, International Preliminary Report on Patentability for International VA (US); Michaela E. Mulhare, Application No. PCT/US2009/067136, issued Jun. 14, 2011 (6 Christiansburg, VA (US) pages). International Search Report for International Application No. PCT/ (73) Assignee: Synthonics, Inc., Blacksburg, VA (US) US2009/067136, mailed Mar. 16, 2010 (2 pages). Islam, M. Saidullet al., “Antibacterial and antifungal activity of mixed (*) Notice: Subject to any disclaimer, the term of this ligand complexes of oxovanadium (IV), titanium (III) and cadmium patent is extended or adjusted under 35 (II) metal ions.” Oriental Journal of Chemistry, 19(3):547-554 U.S.C. 154(b) by 0 days. (2003). Jain, Sangeeta, et al., “Synthesis and biological evaluation of mixed This patent is Subject to a terminal dis ligand complexes of pyrazinamide and isoniazid.” Oriental Journal of claimer. Chemistry, 2002):409-410 (2004). Khan, F. “Study of mixed ligand complexes of Zn(ID with some (21) Appl. No.: 13/542,301 antibiotics and vitamin-B1 drugs, Kinetics of electrode reactions.” Journal of the Indian Chemical Society, 84(7):702-705 (Jul 2007). -
Trust, but Verify: on the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
J. Chem. Inf. Model. XXXX, xxx, 000 A Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research Denis Fourches,† Eugene Muratov,†,‡ and Alexander Tropsha*,† Laboratory for Molecular Modeling, Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599, and Laboratory of Theoretical Chemistry, Department of Molecular Structure, A.V. Bogatsky Physical-Chemical Institute NAS of Ukraine, Odessa, 65080, Ukraine Received May 5, 2010 1. INTRODUCTION to the prediction performances of the derivative QSAR models. They also presented several illustrative examples With the recent advent of high-throughput technologies of incorrect structures generated from either correct or for both compound synthesis and biological screening, there incorrect SMILES. The main conclusions of the study were is no shortage of publicly or commercially available data that small structural errors within a data set could lead to sets and databases1 that can be used for computational drug 2 significant losses of predictive ability of QSAR models. The discovery applications (reviewed recently in Williams et al. ). authors further demonstrated that manual curation of struc- Rapid growth of large, publicly available databases (such 3 4 tural data leads to substantial increase in the model predic- as PubChem or ChemSpider containing more than 20 tivity. This conclusion becomes especially important in light million molecular records each) enabled by experimental of the aforementioned study of Oprea et al.6 that cited a projects such as NIH’s Molecular Libraries and Imaging significant error rate in medicinal chemistry literature. Initiative5 provides new opportunities for the development Alarmed by these conclusions, we have examined several of cheminformatics methodologies and their application to popular public databases of bioactive molecules to assess knowledge discovery in molecular databases. -
Chemoinformatics for Green Chemistry
Contents LIST OF PAPERS .................................................................................... 3 LISTS OF ACRONYMS AND ABBREVIATIONS ............................... 4 INTRODUCTION .................................................................................. 7 BACKGROUND .............................................................................................7 AIMS OF THE THESIS ..................................................................................9 METHODOLOGY ............................................................................... 11 QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS (QSPR)......11 Linear Free Energy Relationships (LFER)...............................................11 Linear Solvation Energy Relationship (LSER).........................................12 Reversed-Phase Liquid Chromatography (RPLC).....................................14 Quantitative Structure-Retention Relationships (QSRR) .........................14 Global Linear Solvation Energy Relationship (LSER)..............................15 Molecular Descriptors ...............................................................................16 The Definition of the Applicability Domain ..............................................17 The Distance-Based Approach Using PLS.......................................17 The Williams Plot .............................................................................18 Associative Neural Networks (ASNN)..............................................19 DATA ANALYSIS ........................................................................................20 -
The Literature of Chemoinformatics: 1978–2018
International Journal of Molecular Sciences Editorial The Literature of Chemoinformatics: 1978–2018 Peter Willett Information School, University of Sheffield, Sheffield S1 4DP, UK; p.willett@sheffield.ac.uk Received: 17 July 2020; Accepted: 31 July 2020; Published: 4 August 2020 Abstract: This article presents a study of the literature of chemoinformatics, updating and building upon an analogous bibliometric investigation that was published in 2008. Data on outputs in the field, and citations to those outputs, were obtained by means of topic searches of the Web of Science Core Collection. The searches demonstrate that chemoinformatics is by now a well-defined sub-discipline of chemistry, and one that forms an essential part of the chemical educational curriculum. There are three core journals for the subject: The Journal of Chemical Information and Modeling, the Journal of Cheminformatics, and Molecular Informatics, and, having established itself, chemoinformatics is now starting to export knowledge to disciplines outside of chemistry. Keywords: bibliometrics; cheminformatics; chemoinformatics; scientometrics 1. Introduction Increasing use is being made of bibliometric methods to analyze the published academic literature, with studies focusing on, e.g., author productivity, the articles appearing in a specific journal, the characteristics of bibliographic frequency distributions, new metrics for research evaluation, and the citations to publications in a specific subject area inter alia (e.g., [1–6]). There have been many bibliometric studies of various aspects of chemistry over the years, with probably the earliest such study being the famous 1926 paper by Lotka in which he discussed author productivity based in part on an analysis of publications in Chemical Abstracts [7].