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Istls Information Services to Life Science Internet Bioinformatics Resources Josef Maier [E-Mail: [email protected]] Last Checked August, 17Th, 2011
IStLS Information Services to Life Science Internet Bioinformatics Resources Josef Maier [e-mail: [email protected]] Last checked August, 17th, 2011 IStLS Bioinformatics Resources http://www.istls.de/bioinfolinks.php Courses and lectures Bioinformatics - Online Courses and Tutorials http://www.bioinformatik.de/cgi-bin/browse/Catalog/Research_and_Education/Online_Courses_and_Tutorials/ EMBRACE Network of Excellence http://www.embracegrid.info/page.php EMBNet Quick Guides http://www.embnet.org/node/64 EMBNet Courses http://www.embnet.org/ Sequence Analysis with distributed Resources http://bibiserv.techfak.uni-bielefeld.de/sadr/ Tutorial Protein Structures (EXPASY) SwissModel http://swissmodel.expasy.org/course/course-index.htm CMBI Courses for protein structure http://swift.cmbi.ru.nl/teach/courses/index.html 2Can Support Portal - Bioinformatics educational resource http://www.ebi.ac.uk/2can Bioconductor Workshops http://www.bioconductor.org/workshops/ CBS Bioinformatics Courses http://www.cbs.dtu.dk/courses.php The European School In Bioinformatics (Biosapiens) http://www.biosapiens.info/page.php?page=esb Institutes Centers Networks Bioinformatics Institutes Germany WSI Wilhelm-Schickard-Institut für Informatik - Universitaet Tuebingen http://www.uni-tuebingen.de/en/faculties/faculty-of-science/departments/computer-science/department.html WSI Huson - Algorithms in Bioinformatics http://www-ab.informatik.uni-tuebingen.de/welcome.html WSI Prof. Zell - Computer Architecture http://www.ra.cs.uni-tuebingen.de/ WSI Kohlbacher - Div. for Simulation -
Open Data, Open Source, and Open Standards in Chemistry: the Blue Obelisk Five Years On" Journal of Cheminformatics Vol
Oral Roberts University Digital Showcase College of Science and Engineering Faculty College of Science and Engineering Research and Scholarship 10-14-2011 Open Data, Open Source, and Open Standards in Chemistry: The lueB Obelisk five years on Andrew Lang Noel M. O'Boyle Rajarshi Guha National Institutes of Health Egon Willighagen Maastricht University Samuel Adams See next page for additional authors Follow this and additional works at: http://digitalshowcase.oru.edu/cose_pub Part of the Chemistry Commons Recommended Citation Andrew Lang, Noel M O'Boyle, Rajarshi Guha, Egon Willighagen, et al.. "Open Data, Open Source, and Open Standards in Chemistry: The Blue Obelisk five years on" Journal of Cheminformatics Vol. 3 Iss. 37 (2011) Available at: http://works.bepress.com/andrew-sid-lang/ 19/ This Article is brought to you for free and open access by the College of Science and Engineering at Digital Showcase. It has been accepted for inclusion in College of Science and Engineering Faculty Research and Scholarship by an authorized administrator of Digital Showcase. For more information, please contact [email protected]. Authors Andrew Lang, Noel M. O'Boyle, Rajarshi Guha, Egon Willighagen, Samuel Adams, Jonathan Alvarsson, Jean- Claude Bradley, Igor Filippov, Robert M. Hanson, Marcus D. Hanwell, Geoffrey R. Hutchison, Craig A. James, Nina Jeliazkova, Karol M. Langner, David C. Lonie, Daniel M. Lowe, Jerome Pansanel, Dmitry Pavlov, Ola Spjuth, Christoph Steinbeck, Adam L. Tenderholt, Kevin J. Theisen, and Peter Murray-Rust This article is available at Digital Showcase: http://digitalshowcase.oru.edu/cose_pub/34 Oral Roberts University From the SelectedWorks of Andrew Lang October 14, 2011 Open Data, Open Source, and Open Standards in Chemistry: The Blue Obelisk five years on Andrew Lang Noel M O'Boyle Rajarshi Guha, National Institutes of Health Egon Willighagen, Maastricht University Samuel Adams, et al. -
The Alexandria Library, a Quantum-Chemical Database of Molecular Properties for Force field Development 9 2017 Received: October 1 1 1 Mohammad M
www.nature.com/scientificdata OPEN Data Descriptor: The Alexandria library, a quantum-chemical database of molecular properties for force field development 9 2017 Received: October 1 1 1 Mohammad M. Ghahremanpour , Paul J. van Maaren & David van der Spoel Accepted: 19 February 2018 Published: 10 April 2018 Data quality as well as library size are crucial issues for force field development. In order to predict molecular properties in a large chemical space, the foundation to build force fields on needs to encompass a large variety of chemical compounds. The tabulated molecular physicochemical properties also need to be accurate. Due to the limited transparency in data used for development of existing force fields it is hard to establish data quality and reusability is low. This paper presents the Alexandria library as an open and freely accessible database of optimized molecular geometries, frequencies, electrostatic moments up to the hexadecupole, electrostatic potential, polarizabilities, and thermochemistry, obtained from quantum chemistry calculations for 2704 compounds. Values are tabulated and where available compared to experimental data. This library can assist systematic development and training of empirical force fields for a broad range of molecules. Design Type(s) data integration objective • molecular physical property analysis objective Measurement Type(s) physicochemical characterization Technology Type(s) Computational Chemistry Factor Type(s) Sample Characteristic(s) 1 Uppsala Centre for Computational Chemistry, Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, SE-75124 Uppsala, Sweden. Correspondence and requests for materials should be addressed to D.v.d.S. (email: [email protected]). -
Molecular Structure Input on the Web Peter Ertl
Ertl Journal of Cheminformatics 2010, 2:1 http://www.jcheminf.com/content/2/1/1 REVIEW Open Access Molecular structure input on the web Peter Ertl Abstract A molecule editor, that is program for input and editing of molecules, is an indispensable part of every cheminfor- matics or molecular processing system. This review focuses on a special type of molecule editors, namely those that are used for molecule structure input on the web. Scientific computing is now moving more and more in the direction of web services and cloud computing, with servers scattered all around the Internet. Thus a web browser has become the universal scientific user interface, and a tool to edit molecules directly within the web browser is essential. The review covers a history of web-based structure input, starting with simple text entry boxes and early molecule editors based on clickable maps, before moving to the current situation dominated by Java applets. One typical example - the popular JME Molecule Editor - will be described in more detail. Modern Ajax server-side molecule editors are also presented. And finally, the possible future direction of web-based molecule editing, based on tech- nologies like JavaScript and Flash, is discussed. Introduction this trend and input of molecular structures directly A program for the input and editing of molecules is an within a web browser is therefore of utmost importance. indispensable part of every cheminformatics or molecu- In this overview a history of entering molecules into lar processing system. Such a program is known as a web applications will be covered, starting from simple molecule editor, molecular editor or structure sketcher. -
Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department
Spoken Tutorial Project, IIT Bombay Brochure for Chemistry Department Name of FOSS Applications Employability GChemPaint GChemPaint is an editor for 2Dchem- GChemPaint is currently being developed ical structures with a multiple docu- as part of The Chemistry Development ment interface. Kit, and a Standard Widget Tool kit- based GChemPaint application is being developed, as part of Bioclipse. Jmol Jmol applet is used to explore the Jmol is a free, open source molecule viewer structure of molecules. Jmol applet is for students, educators, and researchers used to depict X-ray structures in chemistry and biochemistry. It is cross- platform, running on Windows, Mac OS X, and Linux/Unix systems. For PG Students LaTeX Document markup language and Value addition to academic Skills set. preparation system for Tex typesetting Essential for International paper presentation and scientific journals. For PG student for their project work Scilab Scientific Computation package for Value addition in technical problem numerical computations solving via use of computational methods for engineering problems, Applicable in Chemical, ECE, Electrical, Electronics, Civil, Mechanical, Mathematics etc. For PG student who are taking Physical Chemistry Avogadro Avogadro is a free and open source, Research and Development in Chemistry, advanced molecule editor and Pharmacist and University lecturers. visualizer designed for cross-platform use in computational chemistry, molecular modeling, material science, bioinformatics, etc. Spoken Tutorial Project, IIT Bombay Brochure for Commerce and Commerce IT Name of FOSS Applications / Employability LibreOffice – Writer, Calc, Writing letters, documents, creating spreadsheets, tables, Impress making presentations, desktop publishing LibreOffice – Base, Draw, Managing databases, Drawing, doing simple Mathematical Math operations For Commerce IT Students Drupal Drupal is a free and open source content management system (CMS). -
Report on an NIH Workshop on Ultralarge Chemistry Databases Wendy A
1 Report on an NIH Workshop on Ultralarge Chemistry Databases Wendy A. Warr Wendy Warr & Associates, 6 Berwick Court, Holmes Chapel, Cheshire, CW4 7HZ, United Kingdom. Email: [email protected] Introduction The virtual workshop took place on December 1-3, 2020. It was aimed at researchers, groups, and companies that generate, manage, sell, search, and screen databases of more than one billion small molecules (Figure 1). There were about 550 “attendees” from 37 different countries. Recent advances in computational chemistry have enabled researchers to navigate virtual chemical spaces containing billions of chemical structures, carrying out similarity searches, studying structure-activity relationships (SAR), experimenting with scaffold-hopping, and using other drug discovery methodologies.1 For clarity, one could differentiate “spaces” from “libraries”, and “libraries” from “databases”. Spaces are combinatorially constructed collections of compounds; they are usually very big indeed and it is not possible to enumerate all the precise chemical structures that are covered. Libraries are enumerated collections of full structures: usually fewer than 1010 molecules. Databases are a way to storing libraries, for example, in a relational database management system. Figure 1. Ultralarge chemical databases. (Source: Marcus Gastreich based on the publication by Hoffmann and Gastreich.) This report summarizes talks from about 30 practitioners in the field of ultralarge collections of molecules. The aim is to represent as accurately as possible the information that was delivered by the speakers; the report does not seek to be evaluative. 2 Welcoming remarks; defining a drug discovery gateway Susan Gregurick, Office of Data Science and Strategy, NIH, USA Data should be “findable, accessible, interoperable and reusable” (FAIR)2 and with this in mind, NIH has been creating, curating, integrating, and querying ultralarge chemistry databases. -
Qsar Methods Development, Virtual and Experimental Screening for Cannabinoid Ligand Discovery
QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY by Kyaw Zeyar Myint BS, Biology, BS, Computer Science, Hampden-Sydney College, 2007 Submitted to the Graduate Faculty of School of Medicine in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2012 UNIVERSITY OF PITTSBURGH SCHOOL OF MEDICINE This dissertation was presented by Kyaw Zeyar Myint It was defended on August 20th, 2012 and approved by Dr. Ivet Bahar, Professor, Department of Computational and Systems Biology Dr. Billy W. Day, Professor, Department of Pharmaceutical Sciences Dr. Christopher Langmead, Associate Professor, Department of Computer Science, CMU Dissertation Advisor: Dr. Xiang-Qun Xie, Professor, Department of Pharmaceutical Sciences ii Copyright © by Kyaw Zeyar Myint 2012 iii QSAR METHODS DEVELOPMENT, VIRTUAL AND EXPERIMENTAL SCREENING FOR CANNABINOID LIGAND DISCOVERY Kyaw Zeyar Myint, PhD University of Pittsburgh, 2012 G protein coupled receptors (GPCRs) are the largest receptor family in mammalian genomes and are known to regulate wide variety of signals such as ions, hormones and neurotransmitters. It has been estimated that GPCRs represent more than 30% of current drug targets and have attracted many pharmaceutical industries as well as academic groups for potential drug discovery. Cannabinoid (CB) receptors, members of GPCR superfamily, are also involved in the activation of multiple intracellular signal transductions and their endogenous ligands or cannabinoids have attracted pharmacological research because of their potential therapeutic effects. In particular, the cannabinoid subtype-2 (CB2) receptor is known to be involved in immune system signal transductions and its ligands have the potential to be developed as drugs to treat many immune system disorders without potential psychotic side- effects. -
Flexible Heuristic Algorithm for Automatic Molecule Fragmentation: Application to the UNIFAC Group Contribution Model Simon Müller*
Müller J Cheminform (2019) 11:57 https://doi.org/10.1186/s13321-019-0382-3 Journal of Cheminformatics RESEARCH ARTICLE Open Access Flexible heuristic algorithm for automatic molecule fragmentation: application to the UNIFAC group contribution model Simon Müller* Abstract A priori calculation of thermophysical properties and predictive thermodynamic models can be very helpful for developing new industrial processes. Group contribution methods link the target property to contributions based on chemical groups or other molecular subunits of a given molecule. However, the fragmentation of the molecule into its subunits is usually done manually impeding the fast testing and development of new group contribution methods based on large databases of molecules. The aim of this work is to develop strategies to overcome the challenges that arise when attempting to fragment molecules automatically while keeping the defnition of the groups as simple as possible. Furthermore, these strategies are implemented in two fragmentation algorithms. The frst algorithm fnds only one solution while the second algorithm fnds all possible fragmentations. Both algorithms are tested to frag- ment a database of 20,000 molecules for use with the group contribution model Universal Quasichemical Func- tional Group Activity Coefcients+ (UNIFAC). Comparison of the results with a reference database shows that both algorithms are capable of successfully fragmenting all the molecules automatically. Furthermore, when applying them on a larger database it is shown, that the newly developed algorithms are capable of fragmenting structures previously thought not possible to fragment. Keywords: Molecule fragmentation, Cheminformatics, RDKit, Property prediction, Group contribution method, UNIFAC, Incrementation Introduction named QSPR methods (Quantitative Structure Property Cheminformatics is a growing feld due to the increas- Relationship). -
Chemical Database Projects Delivered by RSC Escience
Chemical Database Projects Delivered by RSC eScience at the FDA Meeting “Development of a Freely Distributable Data System for the Registration of Substances” Antony Williams RSC eScience . What was once just ChemSpider is much more… . ChemSpider Reactions . Chemicals Validation and Standardization Platform . Learn Chemistry Wiki . National Chemical Database Service . Open PHACTS . PharmaSea . Global Chemistry Hub We are known for ChemSpider… . The Free Chemical Database . A central hub for chemists to source information . >28 million unique chemical records . Aggregated from >400 data sources . Chemicals, spectra, CIF files, movies, images, podcasts, links to patents, publications, predictions . A central hub for chemists to deposit & curate data We Want to Answer Questions . Questions a chemist might ask… . What is the melting point of n-heptanol? . What is the chemical structure of Xanax? . Chemically, what is phenolphthalein? . What are the stereocenters of cholesterol? . Where can I find publications about xylene? . What are the different trade names for Ketoconazole? . What is the NMR spectrum of Aspirin? . What are the safety handling issues for Thymol Blue? I want to know about “Vincristine” Vincristine: Identifiers and Properties Vincristine: Vendors and Sources Vincristine: Articles How did we build it? . We deal in Molfiles or SDF files – with coordinates . Deposit anything that has an InChI – we support what InChI can handle, good and bad . Standardization based on “InChI standardization” . InChIs aggregate (certain) tautomers The InChI Identifier Downsides of InChI . Good for small molecules – but no polymers, issues with inorganics, organometallics, imperfect stereochemistry. ChemSpider is “small molecules” . InChI used as the “deduplicator” – FIRST version of a compound into the database becomes THE structure to deduplicate against… Side Effects of InChI Usage SMILES by comparison… Side Effects of InChI Usage Searches: The INTERNET Search by InChI ChemSpider Google Search http://www.chemspider.com/google/ How did we build it? . -
Open Chemoinformatic Resources to Explore the Structure, Properties and Chemical Space of Cite This: RSC Adv.,2017,7,54153 Molecules
RSC Advances REVIEW View Article Online View Journal | View Issue Open chemoinformatic resources to explore the structure, properties and chemical space of Cite this: RSC Adv.,2017,7,54153 molecules a ab a Mariana Gonzalez-Medina,´ J. Jesus´ Naveja, Norberto Sanchez-Cruz´ a and Jose´ L. Medina-Franco * New technologies are shaping the way drug discovery data is analyzed and shared. Open data initiatives and web servers are assisting the analysis of the large amounts of data that we are now able to produce. The final goal is to accelerate the process of moving from new data to useful information that could lead to Received 27th October 2017 treatments for human diseases. This review discusses open chemoinformatic resources to analyze the Accepted 21st November 2017 diversity and coverage of the chemical space of screening libraries and to explore structure–activity DOI: 10.1039/c7ra11831g relationships of screening data sets. Free resources to implement workflows and representative web- rsc.li/rsc-advances based applications are emphasized. Future directions in this field are also discussed. Creative Commons Attribution 3.0 Unported Licence. 1. Introduction connections between biological activities, ligands and proteins.3 During the past few years, there has been an important increase Herein we review representative chemoinformatic tools in open data initiatives to promote the availability of free essential to explore the structure, chemical space and properties research-based tools and information.1 While there is still some of molecules. The review is focused on recent and representative resistance to open data in some chemistry and drug discovery free web-based applications. -
Retro Drug Design: from Target Properties to Molecular Structures
Retro Drug Design: From Target Properties to Molecular Structures Yuhong Wang*, Sam Michael, Ruili Huang, Jinghua Zhao, Katlin Recabo, Danielle Bougie, Qiang Shu, Paul Shinn, Hongmao Sun* National Center for Advancing Translational Sciences (NCATS) 9800 Medical Center Drive, Rockville, MD 20850 *Contact information for the corresponding authors: Yuhong Wang Hongmao Sun, PhD NCATS/NIH NCATS/NIH 9800 Medical Center Dr. 9800 Medical Center Dr. Rockville, MD 20854 Rockville, MD 20854 Phone: 301-480-9855 Phone: 301-480-9839 e-mail: [email protected] e-mail: [email protected] Abstract: To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate µ opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. -
Predicting Outcomes of Catalytic Reactions Using Machine Learning
Predicting outcomes of catalytic reactions using ma- chine learning† Trevor David Rhone,∗a Robert Hoyt,a Christopher R. O’Connor,b Matthew M. Montemore,b,c Challa S.S.R. Kumar,b,c Cynthia M. Friend,b,c and Efthimios Kaxiras a,c Predicting the outcome of a chemical reaction using efficient computational models can be used to develop high-throughput screening techniques. This can significantly reduce the number of experiments needed to be performed in a huge search space, which saves time, effort and ex- pense. Recently, machine learning methods have been bolstering conventional structure-activity relationships used to advance understanding of chemical reactions. We have developed a model to predict the products of catalytic reactions on the surface of oxygen-covered and bare gold using machine learning. Using experimental data, we developed a machine learning model that maps reactants to products, using a chemical space representation. This involves predicting a chemical space value for the products, and then matching this value to a molecular structure chosen from a database. The database was developed by applying a set of possible reaction outcomes using known reaction mechanisms. Our machine learning approach complements chemical intuition in predicting the outcome of several types of chemical reactions. In some cases, machine learn- ing makes correct predictions where chemical intuition fails. We achieve up to 93% prediction accuracy for a small data set of less than two hundred reactions. 1 Introduction oped to aid in reaction prediction, they have generally involved Efficient prediction of reaction products has long been a major encoding this intuition into a set of rules, and have not been goal of the organic chemistry community 1–3 and the drug discov- widely adopted 2,6–9.