Quantum Chemistry with Ghemical: a Guide
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Optimizing the Use of Open-Source Software Applications in Drug
DDT • Volume 11, Number 3/4 • February 2006 REVIEWS TICS INFORMA Optimizing the use of open-source • software applications in drug discovery Reviews Werner J. Geldenhuys1, Kevin E. Gaasch2, Mark Watson2, David D. Allen1 and Cornelis J.Van der Schyf1,3 1Department of Pharmaceutical Sciences, School of Pharmacy,Texas Tech University Health Sciences Center, Amarillo,TX, USA 2West Texas A&M University, Canyon,TX, USA 3Pharmaceutical Chemistry, School of Pharmacy, North-West University, Potchefstroom, South Africa Drug discovery is a time consuming and costly process. Recently, a trend towards the use of in silico computational chemistry and molecular modeling for computer-aided drug design has gained significant momentum. This review investigates the application of free and/or open-source software in the drug discovery process. Among the reviewed software programs are applications programmed in JAVA, Perl and Python, as well as resources including software libraries. These programs might be useful for cheminformatics approaches to drug discovery, including QSAR studies, energy minimization and docking studies in drug design endeavors. Furthermore, this review explores options for integrating available computer modeling open-source software applications in drug discovery programs. To bring a new drug to the market is very costly, with the current of combinatorial approaches and HTS. The addition of computer- price tag approximating US$800 million, according to data reported aided drug design technologies to the R&D approaches of a com- in a recent study [1]. Therefore, it is not surprising that pharma- pany, could lead to a reduction in the cost of drug design and ceutical companies are seeking ways to optimize costs associated development by up to 50% [6,7]. -
Pipenightdreams Osgcal-Doc Mumudvb Mpg123-Alsa Tbb
pipenightdreams osgcal-doc mumudvb mpg123-alsa tbb-examples libgammu4-dbg gcc-4.1-doc snort-rules-default davical cutmp3 libevolution5.0-cil aspell-am python-gobject-doc openoffice.org-l10n-mn libc6-xen xserver-xorg trophy-data t38modem pioneers-console libnb-platform10-java libgtkglext1-ruby libboost-wave1.39-dev drgenius bfbtester libchromexvmcpro1 isdnutils-xtools ubuntuone-client openoffice.org2-math openoffice.org-l10n-lt lsb-cxx-ia32 kdeartwork-emoticons-kde4 wmpuzzle trafshow python-plplot lx-gdb link-monitor-applet libscm-dev liblog-agent-logger-perl libccrtp-doc libclass-throwable-perl kde-i18n-csb jack-jconv hamradio-menus coinor-libvol-doc msx-emulator bitbake nabi language-pack-gnome-zh libpaperg popularity-contest xracer-tools xfont-nexus opendrim-lmp-baseserver libvorbisfile-ruby liblinebreak-doc libgfcui-2.0-0c2a-dbg libblacs-mpi-dev dict-freedict-spa-eng blender-ogrexml aspell-da x11-apps openoffice.org-l10n-lv openoffice.org-l10n-nl pnmtopng libodbcinstq1 libhsqldb-java-doc libmono-addins-gui0.2-cil sg3-utils linux-backports-modules-alsa-2.6.31-19-generic yorick-yeti-gsl python-pymssql plasma-widget-cpuload mcpp gpsim-lcd cl-csv libhtml-clean-perl asterisk-dbg apt-dater-dbg libgnome-mag1-dev language-pack-gnome-yo python-crypto svn-autoreleasedeb sugar-terminal-activity mii-diag maria-doc libplexus-component-api-java-doc libhugs-hgl-bundled libchipcard-libgwenhywfar47-plugins libghc6-random-dev freefem3d ezmlm cakephp-scripts aspell-ar ara-byte not+sparc openoffice.org-l10n-nn linux-backports-modules-karmic-generic-pae -
Bringing Open Source to Drug Discovery
Bringing Open Source to Drug Discovery Chris Swain Cambridge MedChem Consulting Standing on the shoulders of giants • There are a huge number of people involved in writing open source software • It is impossible to acknowledge them all individually • The slide deck will be available for download and includes 25 slides of details and download links – Copy on my website www.cambridgemedchemconsulting.com Why us Open Source software? • Allows access to source code – You can customise the code to suit your needs – If developer ceases trading the code can continue to be developed – Outside scrutiny improves stability and security What Resources are available • Toolkits • Databases • Web Services • Workflows • Applications • Scripts Toolkits • OpenBabel (htttp://openbabel.org) is a chemical toolbox – Ready-to-use programs, and complete programmer's toolkit – Read, write and convert over 110 chemical file formats – Filter and search molecular files using SMARTS and other methods, KNIME add-on – Supports molecular modeling, cheminformatics, bioinformatics – Organic chemistry, inorganic chemistry, solid-state materials, nuclear chemistry – Written in C++ but accessible from Python, Ruby, Perl, Shell scripts… Toolkits • OpenBabel • R • CDK • OpenCL • RDkit • SciPy • Indigo • NumPy • ChemmineR • Pandas • Helium • Flot • FROWNS • GNU Octave • Perlmol • OpenMPI Toolkits • RDKit (http://www.rdkit.org) – A collection of cheminformatics and machine-learning software written in C++ and Python. – Knime nodes – The core algorithms and data structures are written in C ++. Wrappers are provided to use the toolkit from either Python or Java. – Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches. -
Arxiv:2008.04198V1 [Physics.Chem-Ph] 10 Aug 2020
Molecular Force Fields with Gradient-Domain Machine Learning (GDML): Comparison and Synergies with Classical Force Fields Huziel E. Sauceda,1, 2, 3, ∗ Michael Gastegger,2, 4, 3 Stefan Chmiela,2 Klaus-Robert M¨uller,2, 5, 6, 7, y and Alexandre Tkatchenko1, z 1Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg 2Machine Learning Group, Technische Universit¨atBerlin, 10587 Berlin, Germany 3BASLEARN, BASF-TU joint Lab, Technische Universit¨atBerlin, 10587 Berlin, Germany 4DFG Cluster of Excellence \Unifying Systems in Catalysis" (UniSysCat), Technische Universit¨atBerlin, 10623 Berlin, Germany 5Department of Brain and Cognitive Engineering, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, Korea 6Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbr¨ucken,Germany 7Google Research, Brain team, Berlin, Germany (Dated: August 11, 2020) Modern machine learning force fields (ML-FF) are able to yield energy and force predictions at the accuracy of high-level ab initio methods, but at a much lower computational cost. On the other hand, classical molecular mechanics force fields (MM-FF) employ fixed functional forms and tend to be less accurate, but considerably faster and transferable between molecules of the same class. In this work, we investigate how both approaches can complement each other. We contrast the ability of ML-FF for reconstructing dynamic and thermodynamic observables to MM-FFs in order to gain a qualitative understanding of the differences between the two approaches. This analysis enables us to modify the generalized AMBER force field (GAFF) by reparametrizing short-range and bonded interactions with more expressive terms to make them more accurate, without sacrificing the key properties that make MM-FFs so successful. -
Technical Notes All Changes in Fedora 13
Fedora 13 Technical Notes All changes in Fedora 13 Edited by The Fedora Docs Team Copyright © 2010 Red Hat, Inc. and others. The text of and illustrations in this document are licensed by Red Hat under a Creative Commons Attribution–Share Alike 3.0 Unported license ("CC-BY-SA"). An explanation of CC-BY-SA is available at http://creativecommons.org/licenses/by-sa/3.0/. The original authors of this document, and Red Hat, designate the Fedora Project as the "Attribution Party" for purposes of CC-BY-SA. In accordance with CC-BY-SA, if you distribute this document or an adaptation of it, you must provide the URL for the original version. Red Hat, as the licensor of this document, waives the right to enforce, and agrees not to assert, Section 4d of CC-BY-SA to the fullest extent permitted by applicable law. Red Hat, Red Hat Enterprise Linux, the Shadowman logo, JBoss, MetaMatrix, Fedora, the Infinity Logo, and RHCE are trademarks of Red Hat, Inc., registered in the United States and other countries. For guidelines on the permitted uses of the Fedora trademarks, refer to https:// fedoraproject.org/wiki/Legal:Trademark_guidelines. Linux® is the registered trademark of Linus Torvalds in the United States and other countries. Java® is a registered trademark of Oracle and/or its affiliates. XFS® is a trademark of Silicon Graphics International Corp. or its subsidiaries in the United States and/or other countries. All other trademarks are the property of their respective owners. Abstract This document lists all changed packages between Fedora 12 and Fedora 13. -
Bringing Open Source to Drug Discovery
Bringing Open Source to Drug Discovery Chris Swain Cambridge MedChem Consulting Standing on the shoulders of giants • There are a huge number of people involved in writing open source software • It is impossible to acknowledge them all individually • The slide deck will be available for download and includes 25 slides of details and download links – Copy on my website www.cambridgemedchemconsulting.com Why us Open Source software? • Allows access to source code – You can customise the code to suit your needs – If developer ceases trading the code can continue to be developed – Outside scrutiny improves stability and security What Resources are available • Toolkits • Databases • Web Services • Workflows • Applications • Scripts Toolkits • OpenBabel • R • CDK • OpenCL • RDkit • SciPy • Indigo • NumPy • ChemmineR • Pandas • Helium • Flot • FROWNS • GNU Octave • Perlmol • OpenMPI Toolkits • OpenBabel (htttp://openbabel.org) is a chemical toolbox – Ready-to-use programs, and complete programmer's toolkit – Read, write and convert over 110 chemical file formats – Filter and search molecular files using SMARTS and other methods, KNIME add-on – Supports molecular modeling, cheminformatics, bioinformatics – Organic chemistry, inorganic chemistry, solid-state materials, nuclear chemistry – Written in C++ but accessible from Python, Ruby, Perl, Shell scripts… Toolkits • RDKit (http://www.rdkit.org) – A collection of cheminformatics and machine-learning software written in C++ and Python. – Knime nodes – The core algorithms and data structures are written in C ++. Wrappers are provided to use the toolkit from either Python or Java. – Additionally, the RDKit distribution includes a PostgreSQL-based cartridge that allows molecules to be stored in relational database and retrieved via substructure and similarity searches. -
Comparison and Synergies with Classical Force Fields
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields Cite as: J. Chem. Phys. 153, 124109 (2020); https://doi.org/10.1063/5.0023005 Submitted: 25 July 2020 . Accepted: 02 September 2020 . Published Online: 24 September 2020 Huziel E. Sauceda , Michael Gastegger , Stefan Chmiela , Klaus-Robert Müller , and Alexandre Tkatchenko COLLECTIONS Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics. This paper was selected as an Editor’s Pick J. Chem. Phys. 153, 124109 (2020); https://doi.org/10.1063/5.0023005 153, 124109 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields Cite as: J. Chem. Phys. 153, 124109 (2020); doi: 10.1063/5.0023005 Submitted: 25 July 2020 • Accepted: 2 September 2020 • Published Online: 24 September 2020 Huziel E. Sauceda,1,2,3,a) Michael Gastegger,2,3,4 Stefan Chmiela,2 Klaus-Robert Müller,2,5,6,7,a) and Alexandre Tkatchenko1,a) AFFILIATIONS 1 Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg 2Machine Learning Group, Technische Universität Berlin, 10587 Berlin, Germany 3BASLEARN, BASF-TU Joint Lab, Technische Universität Berlin, 10587 Berlin, Germany 4DFG Cluster of Excellence “Unifying Systems in Catalysis” (UniSysCat), Technische Universität Berlin, 10623 Berlin, Germany 5Department of Artificial Intelligence, Korea University, Anam-dong, Seongbuk-gu, Seoul 136-713, South Korea 6Max Planck Institute for Informatics, Stuhlsatzenhausweg, 66123 Saarbrücken, Germany 7Google Research, Brain Team, Berlin, Germany Note: This paper is part of the JCP Special Topic on Machine Learning Meets Chemical Physics. -
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Handbook [Html]
The MDP Engineer’s Desktop and Library Peter Long, Paul Fidler, Tim Froggatt Cambridge University Engineering Department Trumpington St. Cambridge CB2 1PZ July 2008 DRAFT 1 2 MDP Engineers Desktop and Library Cambridge-MIT Institute The Cambridge-MIT Institute (CMI) is a pioneering partnership between Cambridge University and the Massachusetts Institute of Technology (MIT). It was set up in 2000 to enhance the competitiveness, productivity and entrepreneurship of the UK economy by improving the effectiveness of knowledge exchange between university and industry, educating leaders and developing programmes for change in universities, industry and government. CMI focuses primarily on business, competitiveness, research and education, and developing strong relationships between these areas. Students, for example, benefit from CMI development and delivery of unique educational programmes for undergraduates, graduates and executives working in industry, including an undergraduate student exchange, an undergraduate research opportunities programme (UROP), CMI Enterprises entrepreneurship programme and several master’s degree programmes. Contact Details : Cambridge-MIT Institute :- http://www.cambridge-mit.org License Agreement - PLEASE READ If you wish to use the ED&L system you must accept the following agreement. Your acceptance of these terms is implied upon use. If you do not agree with these terms or the terms of the Licences under which many of its components are released, please do not use the CD/DVD/USB. Neither the University of Cambridge, the Massachusetts Institute of Technology (MIT) or the Cambridge- MIT Institute (CMI), nor any person who has been involved in the creation, production or distribution of the MDP EDaL CD/DVD/USB makes any warranties or representations with respect to the MDP EDaL CD/DVD/USB and any of its contents whether express or implied by statute, common law, trade usage, course of dealing or otherwise all of which are hereby excluded to the fullest extent of the law. -
Building, Installing and Running Software
Building, installing and running software Day one Bob Dowling University Computing Service http://www-uxsup.csx.cam.ac.uk/courses/ http://training.csx.cam.ac.uk/ 1 Welcome to the UCS course on “Building, installing and running software”. This course is given on the PWF Linux system though the skills it teaches should be portable to any modern Unix platform and certainly to other GNU/Linux platforms. When this course is given in classrooms students are provided with course accounts. These are not compulsory and you may use your personal account. If you are a seasoned Unix user and have your own personal .bashrc file you will need to copy it somewhere safe; it will be overwritten in this course. Why do this course? It's the standard It's my software in the … supervisor's community. code. “other people's software” I need a I found it newer on Google. version. 2 This course is designed to help you with software that's been dumped on you by your supervisor, or that you tracked down because someone told you it would be useful, or that you found when you were looking for something else. What will you learn? Building software in your home Installing software directory no system Running software privileges 3 This course will give you the skills to look after that software yourself without needing to pester your system administrator, without having to have system privileges and without having to leave your home directory! What is this course not for? System administration System directories Writing software 4 This is not a course for system administrators. -
Secure Content Distribution Using Untrusted Servers Kevin Fu
Secure content distribution using untrusted servers Kevin Fu MIT Computer Science and Artificial Intelligence Lab in collaboration with M. Frans Kaashoek (MIT), Mahesh Kallahalla (DoCoMo Labs), Seny Kamara (JHU), Yoshi Kohno (UCSD), David Mazières (NYU), Raj Rajagopalan (HP Labs), Ron Rivest (MIT), Ram Swaminathan (HP Labs) For Peter Szolovits slide #1 January-April 2005 How do we distribute content? For Peter Szolovits slide #2 January-April 2005 We pay services For Peter Szolovits slide #3 January-April 2005 We coerce friends For Peter Szolovits slide #4 January-April 2005 We coerce friends For Peter Szolovits slide #4 January-April 2005 We enlist volunteers For Peter Szolovits slide #5 January-April 2005 Fast content distribution, so what’s left? • Clients want ◦ Authenticated content ◦ Example: software updates, virus scanners • Publishers want ◦ Access control ◦ Example: online newspapers But what if • Servers are untrusted • Malicious parties control the network For Peter Szolovits slide #6 January-April 2005 Taxonomy of content Content Many-writer Single-writer General purpose file systems Many-reader Single-reader Content distribution Personal storage Public Private For Peter Szolovits slide #7 January-April 2005 Framework • Publishers write➜ content, manage keys • Clients read/verify➜ content, trust publisher • Untrusted servers replicate➜ content • File system protects➜ data and metadata For Peter Szolovits slide #8 January-April 2005 Contributions • Authenticated content distribution SFSRO➜ ◦ Self-certifying File System Read-Only -
Geneious Prime 2020.0 User Manual
Geneious Prime 2020.0 User Manual Biomatters Ltd January 14, 2020 Contents 1 Getting Started7 1.1 Downloading & Installing Geneious Prime ..................... 7 1.2 Geneious Prime setup ................................. 9 1.3 Upgrading to new versions .............................. 16 1.4 Licensing......................................... 17 1.5 Troubleshooting..................................... 19 2 The Geneious Prime Main Window 21 2.1 The Sources Panel.................................... 22 2.2 The Document Table .................................. 22 2.3 The Document Viewer Panel.............................. 25 2.4 The Toolbar ....................................... 26 2.5 The Help Panel ..................................... 27 2.6 Geneious Prime menu bar options .......................... 29 3 Importing and Exporting Data 35 3.1 Importing data from the hard drive to your Local folders............. 35 3.2 Data input formats ................................... 36 3.3 Importing files from public databases ........................ 44 3.4 Agents .......................................... 47 3.5 Exporting files...................................... 50 3.6 Printing and Saving Images.............................. 52 4 Managing Your Local Documents 55 4.1 Organizing your local documents........................... 55 4.2 Searching and filtering local documents....................... 58 4.3 Find Duplicates..................................... 62 4.4 Batch Rename...................................... 63 4.5 Backing up your local documents..........................