Open Chemoinformatic Resources to Explore the Structure, Properties and Chemical Space of Molecules
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Wannier90 As a Community Code: New
IOP Journal of Physics: Condensed Matter Journal of Physics: Condensed Matter J. Phys.: Condens. Matter J. Phys.: Condens. Matter 32 (2020) 165902 (25pp) https://doi.org/10.1088/1361-648X/ab51ff 32 Wannier90 as a community code: new 2020 features and applications © 2020 IOP Publishing Ltd Giovanni Pizzi1,29,30 , Valerio Vitale2,3,29 , Ryotaro Arita4,5 , Stefan Bl gel6 , Frank Freimuth6 , Guillaume G ranton6 , JCOMEL ü é Marco Gibertini1,7 , Dominik Gresch8 , Charles Johnson9 , Takashi Koretsune10,11 , Julen Ibañez-Azpiroz12 , Hyungjun Lee13,14 , 165902 Jae-Mo Lihm15 , Daniel Marchand16 , Antimo Marrazzo1 , Yuriy Mokrousov6,17 , Jamal I Mustafa18 , Yoshiro Nohara19 , 4 20 21 G Pizzi et al Yusuke Nomura , Lorenzo Paulatto , Samuel Poncé , Thomas Ponweiser22 , Junfeng Qiao23 , Florian Thöle24 , Stepan S Tsirkin12,25 , Małgorzata Wierzbowska26 , Nicola Marzari1,29 , Wannier90 as a community code: new features and applications David Vanderbilt27,29 , Ivo Souza12,28,29 , Arash A Mostofi3,29 and Jonathan R Yates21,29 Printed in the UK 1 Theory and Simulation of Materials (THEOS) and National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne (EPFL), CH-1015 CM Lausanne, Switzerland 2 Cavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, United Kingdom 10.1088/1361-648X/ab51ff 3 Departments of Materials and Physics, and the Thomas Young Centre for Theory and Simulation of Materials, Imperial College London, London SW7 2AZ, United Kingdom 4 RIKEN -
Accelerating Molecular Docking by Parallelized Heterogeneous Computing – a Case Study of Performance, Quality of Results
solis vasquez, leonardo ACCELERATINGMOLECULARDOCKINGBY PARALLELIZEDHETEROGENEOUSCOMPUTING–A CASESTUDYOFPERFORMANCE,QUALITYOF RESULTS,ANDENERGY-EFFICIENCYUSINGCPUS, GPUS,ANDFPGAS Printed and/or published with the support of the German Academic Exchange Service (DAAD). ACCELERATINGMOLECULARDOCKINGBYPARALLELIZED HETEROGENEOUSCOMPUTING–ACASESTUDYOF PERFORMANCE,QUALITYOFRESULTS,AND ENERGY-EFFICIENCYUSINGCPUS,GPUS,ANDFPGAS at the Computer Science Department of the Technische Universität Darmstadt submitted in fulfilment of the requirements for the degree of Doctor of Engineering (Dr.-Ing.) Doctoral thesis by Solis Vasquez, Leonardo from Lima, Peru Reviewers Prof. Dr.-Ing. Andreas Koch Prof. Dr. Christian Plessl Date of the oral exam October 14, 2019 Darmstadt, 2019 D 17 Solis Vasquez, Leonardo: Accelerating Molecular Docking by Parallelized Heterogeneous Computing – A Case Study of Performance, Quality of Re- sults, and Energy-Efficiency using CPUs, GPUs, and FPGAs Darmstadt, Technische Universität Darmstadt Date of the oral exam: October 14, 2019 Please cite this work as: URN: urn:nbn:de:tuda-tuprints-92886 URL: https://tuprints.ulb.tu-darmstadt.de/id/eprint/9288 This document is provided by TUprints, The Publication Service of the Technische Universität Darmstadt https://tuprints.ulb.tu-darmstadt.de This work is licensed under a Creative Commons “Attribution-ShareAlike 4.0 In- ternational” license. ERKLÄRUNGENLAUTPROMOTIONSORDNUNG §8 Abs. 1 lit. c PromO Ich versichere hiermit, dass die elektronische Version meiner Disserta- tion mit der schriftlichen Version übereinstimmt. §8 Abs. 1 lit. d PromO Ich versichere hiermit, dass zu einem vorherigen Zeitpunkt noch keine Promotion versucht wurde. In diesem Fall sind nähere Angaben über Zeitpunkt, Hochschule, Dissertationsthema und Ergebnis dieses Versuchs mitzuteilen. §9 Abs. 1 PromO Ich versichere hiermit, dass die vorliegende Dissertation selbstständig und nur unter Verwendung der angegebenen Quellen verfasst wurde. -
Universid Ade De Sã O Pa
A hardware/software codesign for the chemical reactivity of BRAMS Carlos Alberto Oliveira de Souza Junior Dissertação de Mestrado do Programa de Pós-Graduação em Ciências de Computação e Matemática Computacional (PPG-CCMC) UNIVERSIDADE DE SÃO PAULO DE SÃO UNIVERSIDADE Instituto de Ciências Matemáticas e de Computação Instituto Matemáticas de Ciências SERVIÇO DE PÓS-GRADUAÇÃO DO ICMC-USP Data de Depósito: Assinatura: ______________________ Carlos Alberto Oliveira de Souza Junior A hardware/software codesign for the chemical reactivity of BRAMS Master dissertation submitted to the Instituto de Ciências Matemáticas e de Computação – ICMC- USP, in partial fulfillment of the requirements for the degree of the Master Program in Computer Science and Computational Mathematics. FINAL VERSION Concentration Area: Computer Science and Computational Mathematics Advisor: Prof. Dr. Eduardo Marques USP – São Carlos August 2017 Ficha catalográfica elaborada pela Biblioteca Prof. Achille Bassi e Seção Técnica de Informática, ICMC/USP, com os dados fornecidos pelo(a) autor(a) Souza Junior, Carlos Alberto Oliveira de S684h A hardware/software codesign for the chemical reactivity of BRAMS / Carlos Alberto Oliveira de Souza Junior; orientador Eduardo Marques. -- São Carlos, 2017. 109 p. Dissertação (Mestrado - Programa de Pós-Graduação em Ciências de Computação e Matemática Computacional) -- Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, 2017. 1. Hardware. 2. FPGA. 3. OpenCL. 4. Codesign. 5. Heterogeneous-computing. I. Marques, Eduardo, orient. II. Título. Carlos Alberto Oliveira de Souza Junior Um coprojeto de hardware/software para a reatividade química do BRAMS Dissertação apresentada ao Instituto de Ciências Matemáticas e de Computação – ICMC-USP, como parte dos requisitos para obtenção do título de Mestre em Ciências – Ciências de Computação e Matemática Computacional. -
Open Babel Documentation Release 2.3.1
Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options ................................................. -
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. -
Computer-Assisted Catalyst Development Via Automated Modelling of Conformationally Complex Molecules
www.nature.com/scientificreports OPEN Computer‑assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Sibo Lin1*, Jenna C. Fromer2, Yagnaseni Ghosh1, Brian Hanna1, Mohamed Elanany3 & Wei Xu4 Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring signifcant researcher time to implement. We automate this workfow using all open‑source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious efects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save signifcant experimental efort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔGPPN), and establish a strong correlation between ΔGPPN and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving signifcant time by ruling out candidates with non‑trivial synthetic routes and poor expected catalytic performance. Quantum mechanical methods with high energy accuracy, such as density functional theory (DFT), can opti- mize molecular input structures to a nearby local minimum, but calculating accurate reaction thermodynamics requires fnding global minimum energy structures1,2. For simple molecules, expert intuition can identify a few minima to focus study on, but an alternative approach must be considered for more complex molecules or to eventually fulfl the dream of autonomous catalyst design 3,4: the potential energy surface must be frst surveyed with a computationally efcient method; then minima from this survey must be refned using slower, more accurate methods; fnally, for molecules possessing low-frequency vibrational modes, those modes need to be treated appropriately to obtain accurate thermodynamic energies 5–7. -
Designing Universal Chemical Markup (UCM) Through the Reusable Methodology Based on Analyzing Existing Related Formats
Designing Universal Chemical Markup (UCM) through the reusable methodology based on analyzing existing related formats Background: In order to design concepts for a new general-purpose chemical format we analyzed the strengths and weaknesses of current formats for common chemical data. While the new format is discussed more in the next article, here we describe our software s t tools and two stage analysis procedure that supplied the necessary information for the n i r development. The chemical formats analyzed in both stages were: CDX, CDXML, CML, P CTfile and XDfile. In addition the following formats were included in the first stage only: e r P CIF, InChI, NCBI ASN.1, NCBI XML, PDB, PDBx/mmCIF, PDBML, SMILES, SLN and Mol2. Results: A two stage analysis process devised for both XML (Extensible Markup Language) and non-XML formats enabled us to verify if and how potential advantages of XML are utilized in the widely used general-purpose chemical formats. In the first stage we accumulated information about analyzed formats and selected the formats with the most general-purpose chemical functionality for the second stage. During the second stage our set of software quality requirements was used to assess the benefits and issues of selected formats. Additionally, the detailed analysis of XML formats structure in the second stage helped us to identify concepts in those formats. Using these concepts we came up with the concise structure for a new chemical format, which is designed to provide precise built-in validation capabilities and aims to avoid the potential issues of analyzed formats. -
Open Data, Open Source and Open Standards in Chemistry: the Blue Obelisk five Years On
Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk ¯ve years on Noel M O'Boyle¤1 , Rajarshi Guha2 , Egon L Willighagen3 , Samuel E Adams4 , Jonathan Alvarsson5 , Richard L Apodaca6 , Jean-Claude Bradley7 , Igor V Filippov8 , Robert M Hanson9 , Marcus D Hanwell10 , Geo®rey R Hutchison11 , Craig A James12 , Nina Jeliazkova13 , Andrew SID Lang14 , Karol M Langner15 , David C Lonie16 , Daniel M Lowe4 , J¶er^omePansanel17 , Dmitry Pavlov18 , Ola Spjuth5 , Christoph Steinbeck19 , Adam L Tenderholt20 , Kevin J Theisen21 , Peter Murray-Rust4 1Analytical and Biological Chemistry Research Facility, Cavanagh Pharmacy Building, University College Cork, College Road, Cork, Co. Cork, Ireland 2NIH Center for Translational Therapeutics, 9800 Medical Center Drive, Rockville, MD 20878, USA 3Division of Molecular Toxicology, Institute of Environmental Medicine, Nobels vaeg 13, Karolinska Institutet, 171 77 Stockholm, Sweden 4Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lens¯eld Road, CB2 1EW, UK 5Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 751 24 Uppsala, Sweden 6Metamolecular, LLC, 8070 La Jolla Shores Drive #464, La Jolla, CA 92037, USA 7Department of Chemistry, Drexel University, 32nd and Chestnut streets, Philadelphia, PA 19104, USA 8Chemical Biology Laboratory, Basic Research Program, SAIC-Frederick, Inc., NCI-Frederick, Frederick, MD 21702, USA 9St. Olaf College, 1520 St. Olaf Ave., North¯eld, MN 55057, USA 10Kitware, Inc., 28 Corporate Drive, Clifton Park, NY 12065, USA 11Department of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, PA 15260, USA 12eMolecules Inc., 380 Stevens Ave., Solana Beach, California 92075, USA 13Ideaconsult Ltd., 4.A.Kanchev str., So¯a 1000, Bulgaria 14Department of Engineering, Computer Science, Physics, and Mathematics, Oral Roberts University, 7777 S. -
Conformational Analysis 3D Structures, Conformations and Molecular Surfaces
Conformational Analysis 3D Structures, Conformations and Molecular Surfaces Christof H. Schwab Molecular Networks GmbH Altamira LLC Henkestr. 91 1455 Candlewood Drive 91052 Erlangen, Germany mn-am.com Columbus, Ohio 43235, USA Molecular Networks and Altamira MN-AM Erlangen, Germany Columbus, Ohio, USA Friedrich-Alexander-Universität The Ohio State University 1997 2008 Chemoinformatics 3D structure generation Physicochemical and reaction properties Metabolic reaction knowledge Computational toxicology and risk assessment Database and knowledgebase Predictive models Consulting services 2 Product Lines – Chemoinformatics CORINA Classic CORINA Classic Industry-standard 3D structure generation CORINA Symphony Profiling and managing of chemical datasets Workflows Structure cleaning/processing Descriptor generation (properties and fragments) SYLVIA Estimation of synthetic accessibility of compounds Public tools ChemoTyper (with ToxPrint Chemotypes) https://chemotyper.org, https://toxprint.org 3 Product Lines – Computational Toxicology and Risk Assessment ChemTunes Platform to support decision making in human health and regulatory critical endpoints Toxicity database (all endpoints) "Inventory" concept for compound location ChemTunes ToxGPS Prediction models All human health related endpoints Workflows TTC (thresholds of toxicological concern) Genotoxic Impurities Read-Across (in development) Workflow ICH M7 GTI (in development) 4 3D Structures, Conformations and Molecular Surfaces – Overview 3D structures – why, -
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. -
Open Source Molecular Modeling
Accepted Manuscript Title: Open Source Molecular Modeling Author: Somayeh Pirhadi Jocelyn Sunseri David Ryan Koes PII: S1093-3263(16)30118-8 DOI: http://dx.doi.org/doi:10.1016/j.jmgm.2016.07.008 Reference: JMG 6730 To appear in: Journal of Molecular Graphics and Modelling Received date: 4-5-2016 Accepted date: 25-7-2016 Please cite this article as: Somayeh Pirhadi, Jocelyn Sunseri, David Ryan Koes, Open Source Molecular Modeling, <![CDATA[Journal of Molecular Graphics and Modelling]]> (2016), http://dx.doi.org/10.1016/j.jmgm.2016.07.008 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Open Source Molecular Modeling Somayeh Pirhadia, Jocelyn Sunseria, David Ryan Koesa,∗ aDepartment of Computational and Systems Biology, University of Pittsburgh Abstract The success of molecular modeling and computational chemistry efforts are, by definition, de- pendent on quality software applications. Open source software development provides many advantages to users of modeling applications, not the least of which is that the software is free and completely extendable. In this review we categorize, enumerate, and describe available open source software packages for molecular modeling and computational chemistry. 1. Introduction What is Open Source? Free and open source software (FOSS) is software that is both considered \free software," as defined by the Free Software Foundation (http://fsf.org) and \open source," as defined by the Open Source Initiative (http://opensource.org). -
3Dmolnet: a Generative Network for Molecular Structures
3DMolNet: A Generative Network for Molecular Structures Vitali Nesterov, Mario Wieser, Volker Roth Department of Mathematics and Computer Science University of Basel, Switzerland [email protected] Abstract With the recent advances in machine learning for quan- tum chemistry, it is now possible to predict the chemical properties of compounds and to generate novel molecules. Existing generative models mostly use a string- or graph- based representation, but the precise three-dimensional co- ordinates of the atoms are usually not encoded. First at- tempts in this direction have been proposed, where autore- gressive or GAN-based models generate atom coordinates. Those either lack a latent space in the autoregressive setting, such that a smooth exploration of the compound space is not possible, or cannot generalize to varying chemical compo- sitions. We propose a new approach to efficiently generate molecular structures that are not restricted to a fixed size or composition. Our model is based on the variational autoen- coder which learns a translation-, rotation-, and permutation- invariant low-dimensional representation of molecules. Our experiments yield a mean reconstruction error below 0.05 A,˚ outperforming the current state-of-the-art methods by a fac- tor of four, and which is even lower than the spatial quanti- zation error of most chemical descriptors. The compositional and structural validity of newly generated molecules has been confirmed by quantum chemical methods in a set of experi- Figure 1: A schematic illustration of the decoding process of ments. the 3DMolNet. The model learns a low-dimensional repre- sentation Z of molecules (red plane). Each latent encoding can be decoded to a nuclear charge matrix C, an Euclidean Introduction distance matrix D, and a bond matrix B using neural net- An exploration of the chemical compound space (CCS) is works (blue dotted arrows).