DOCSLIB.ORG

Chemistry in Times of Artificial Intelligence

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chemistry in Times of Artificial Intelligence Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 1 2 3 Chemistry in Times of Artificial Intelligence 4 [a] 5 Johann Gasteiger* 6 7 Dedicated to the memory of Professor Rolf Huisgen who passed away on March 26, 2020, shortly before his 100th birthday. 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 ChemPhysChem 2020, 21, 2233– 2242 2233 © 2020 The Authors. Published by Wiley-VCH GmbH Wiley VCH Montag, 05.10.2020 2020 / 179989 [S. 2233/2242] 1 Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 1 Chemists have to a large extent gained their knowledge by the elucidation of the structure of molecules. This eventually 2 doing experiments and thus gather data. By putting various led to a discipline of its own: chemoinformatics. Chemo- 3 data together and then analyzing them, chemists have fostered informatics has found important applications in the fields of 4 their understanding of chemistry. Since the 1960s, computer drug discovery, analytical chemistry, organic chemistry, agri- 5 methods have been developed to perform this process from chemical research, food science, regulatory science, material 6 data to information to knowledge. Simultaneously, methods science, and process control. From its inception, chemoinfor- 7 were developed for assisting chemists in solving their funda- matics has utilized methods from artificial intelligence, an 8 mental questions such as the prediction of chemical, physical, approach that has recently gained more momentum. 9 or biological properties, the design of organic syntheses, and 10 11 1. Introduction accelerate chemical innovation. It will further be shown where 12 new methods from artificial intelligence are introduced into 13 Artificial Intelligence (AI) has entered many domains of society, various fields of chemistry to further assist in understanding 14 and artificial intelligence methods are used for such diverse chemical data. 15 tasks as human speech recognition, successfully competing 16 with experts in strategic games (like chess and GO), and 17 autonomously operating cars. These methods essentially derive 2. Learning in Chemistry 18 their power by learning from data and are sometimes called 19 machine learning or even deep learning. Fortunately, concomitant with this vast increase in chemical 20 Chemistry has from the very beginning derived its knowl- data, computer technology arrived and rapidly became more 21 edge from data. Chemists have run experiments to obtain data and more powerful. Thus, computers could be used to make 22 on chemical or physical properties, on chemical reactions, or on mathematical operations solving equations such as those 23 biological activities. These data were then used to make encountered in quantum mechanics (QM), the theory that 24 predictions by analogy or to derive models for the principles underlies chemistry. This allowed the calculation of physical 25 that underly the data. To foster an understanding of chemistry and chemical data by QM methods of increasing complexity. 26 in students Rolf Huisgen has written a chapter “Mesomerie- This is deductive learning, learning from a theory to produce 27 Lehre” for a textbook on laboratory experiments.[1] It should, data. 28 however, be recognized that the concepts of inductive and However, it was also realized that a computer operates on a 29 resonance effect contained in this chapter were not derived bit level and thus can be used for logical operations. 30 from any theory but were an attempt to order the observations Furthermore, software can be developed that allow the 31 and data on product distributions and reaction rates in electro- processing of data and information. Thus, computers can be 32 philic aromatic substitution. used for inductive learning (Figure 1): data can be put together 33 By doing experiments, chemists have amassed a huge to generate information and many pieces of information can be 34 amount of data on chemical structures and their properties. In generalized to produce knowledge. 35 1971, about one million substances were registered in the As an example, the measurement of the biological activity 36 Chemical Abstracts Service STN database and our supervisor of a compound is only of much use when the structure of the 37 Rolf Huisgen gave us the impression that he knew whether a compound is known; this is then information, putting the 38 compound was known or not–and we could not find a case activity data in the context of the chemical structure. Several 39 where he was wrong. While this may have been feasible with sets of structures and their corresponding biological activities 40 one million compounds it is definitely not possible any more 41 now with 160 million organic and inorganic substances and 68 42 million protein and nucleic acid sequences in the CAS database. 43 This review will show how the methods of chemoinfor- 44 matics have made accessible this huge amount of data and 45 information and how these data can be converted into knowl- 46 edge to increase our understanding of chemistry and to 47 48 49 [a] Prof. Dr. J. Gasteiger 50 Computer-Chemie-Centrum and Institute of Organic Chemistry 51 University of Erlangen-Nuremberg 52 Naegelsbachstrasse 25, 91052 Erlangen, Germany E-mail: [email protected] 53 © 2020 The Authors. Published by Wiley-VCH GmbH. This is an open 54 access article under the terms of the Creative Commons Attribution Non- 55 Commercial License, which permits use, distribution and reproduction in 56 any medium, provided the original work is properly cited and is not used for commercial purposes. Figure 1. Deductive and inductive learning. 57 ChemPhysChem 2020, 21, 2233– 2242 www.chemphyschem.org 2234 © 2020 The Authors. Published by Wiley-VCH GmbH Wiley VCH Montag, 05.10.2020 2020 / 179989 [S. 2234/2242] 1 Reviews ChemPhysChem doi.org/10.1002/cphc.202000518 can then be analyzed and generalized to produce an under- were, and still are being, established.[5,6] The challenge of 1 standing, knowledge, of the relationships between structure computer-assisted synthesis design was taken up early on.[7] 2 and biological activity. The third question needs automatic procedures for structure 3 elucidation.[8,9] 4 These fundamental questions of a chemist have been the 5 2.1. Chemoinformatics driving forces of a lot of work in chemoinformatic in the last 6 few decades which will be reported in some of the following 7 Starting in the 1960s, computer methods were developed that chapters. 8 allowed one to perform inductive learning in chemistry, a field 9 that later became known as Chemoinformatics.[2,3] First, meth- 10 ods had to be developed for the computer representation of 2.2. Artificial Intelligence 11 chemical structures and reactions. Then, procedures had to be 12 utilized or developed for inductive learning, a field that was It was realized from early on that the development of systems 13 coined as chemometrics and encompassed methods from for property prediction, synthesis design, or structure elucida- 14 statistics and pattern recognition. Chemometrics methods were tion are quite demanding tasks and would require a lot of 15 applied to the analysis of data from analytical chemistry.[4] conceptual work and state of the art computer technology. 16 However, work was also initiated to tackle quite difficult tasks Therefore, emerging methods from computer science found 17 such as those embedded in the fundamental questions of a their early applications in chemistry. This is true for methods 18 chemist: that were subsumed under the name of artificial intelligence 19 1) What structure do I need for a desired property? and publications with titles such as “Applications of Artificial 20 2) How can I synthesize this structure? Intelligence for Chemical Inference” appeared in the context of 21 3) What is the outcome of my reaction? the DENDRAL project at Stanford University.[10] The DENDRAL 22 For answering a question on property predictions quantita- project developed methods for predicting the structure of a 23 tive structure property/activity relationships (QSPR and QSAR) compound from its mass spectrum. In spite of the collaboration 24 of some highly reputed chemists and computer scientists and a 25 lot of work put into its development, the DENDRAL project was 26 Johann Gasteiger has studied chemistry at eventually discontinued. Many reasons might be found for that 27 the Ludwig-Maximilians-University of Munich decision, not the least that the field of artificial intelligence had 28 and the University of Zürich. He obtained his lost its promise and reputation in the late 1970s. In recent years, 29 PhD in 1971 at the University of Munich under the guidance of Prof. Dr. Rolf Huisgen a renaissance of artificial intelligence in general and of its 30 with studies on the chemistry of cyclooctate- application in chemistry, in particular, can be observed. Several 31 traene and homotropylium ions. He then reasons have contributed to this development: availability of 32 went as a postdoc to Prof. Andrew Streitwies- large amounts of data, increase in computer power, and new 33 er Jr. at the University of California in Berkeley methods for processing these data. As these methods all are 34 where he did ab initio calculations on carbanions. In 1972 he joined the research based on computer processing this field has also often been 35 group of Prof. Ivar Ugi at the Technical referred to “machine learning”. There is no clear distinction 36 University of Munich, working on a computer between these two terms although the term artificial intelli- 37 program for the planning of organic synthe- gence seems to be the more comprehensive one. 38 ses.
Load more

Recommended publications
  • Automated Calculation of Reaction Kinetics Via Transition State Theory
    AUTOMATED CALCULATION OF REACTION KINETICS VIA TRANSITION STATE THEORY A Dissertation Presented By Pierre Lennox Bhoorasingh to The Department of Chemical Engineering In partial fulfillment of the requirements for the degree of Doctor of Philosophy in the field of Chemical Engineering Northeastern University Boston, Massachusetts August 2016 Dedication I dedicate this thesis to AMT. i Acknowledgments I have been able to complete this thesis work due to the help I have received from those who have found time in their busy schedules. This is my attempt to express my profound gratitude to those who have helped me during my thesis work. Thanks to my advisor, Prof. Richard West, for the guidance over the 5 years. You also gave me the freedom to explore and that has only enhanced my thesis work, and it has been a pleasure to be your first graduate student. I would also like to thank my thesis committee members, Dr. David Budil, Dr. Hicham Fenniri, Dr. C. Franklin Goldsmith, and Dr. Reza Sheikhi. They made the time to have engaging discussions that impacted this thesis, and were also very generous with their professional advice. Thanks to the Computational Modeling group. Fariba Seyedzadeh Khanshan and Be- linda Slakman, you were always helpful in our discussions and made the laboratory a fun working environment. I’d also like to thank Jason Cain for being a super helpful under- graduate who assumed nothing in pursuit of the right approach. I want to also thank Sean Troiano, Victor Lambert, Jacob Barlow, and Elliot Nash for their contributions to laboratory discussions.
    [Show full text]
  • Neural Networks in Chemistry
    Neural Networks in Chemistry By Johann Gasteiger” and Jure Zupan” The capabilities of the human brain have always fascinated scientists and led them to investi- gate its inner workings. Over the past 50 years a number of models have been developed which have attempted to replicate the brain’s various functions. At the same time the development of computers was taking a totally different direction. As a result, today’s computer architec- tures, operating systems, and programming have very little in common with information processing as performed by the brain. Currently we are experiencing a reevaluation of the brain’s abilities, and models of information processing in the brain have been translated into algorithms and made widely available. The basic building-block of these brain models (neural networks) is an information processing unit that is a model of a neuron. An artificial neuron of this kind performs only rather simple mathematical operations; its effectiveness is derived solely from the way in which large numbers of neurons may be connected to form a network. Just as the various neural models replicate different abilities of the brain, they can be used to solve different types of problem: the classification of objects, the modeling of functional relationships, the storage and retrieval of information, and the representation of large amounts of data. This potential suggests many possibilities for the processing of chemical data, and already applications cover a wide area: spectroscopic analysis, prediction of reactions, chem- ical process control, and the analysis of electrostatic potentials. All these are just a small sample of the great many possibilities.
    [Show full text]
  • NIH Public Access Author Manuscript J Med Chem
    NIH Public Access Author Manuscript J Med Chem. Author manuscript; available in PMC 2015 June 26. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: J Med Chem. 2014 June 26; 57(12): 4977–5010. doi:10.1021/jm4004285. QSAR Modeling: Where have you been? Where are you going to? Artem Cherkasov1, Eugene N. Muratov2,3, Denis Fourches2, Alexandre Varnek4, Igor I. Baskin5, Mark Cronin6, John Dearden6, Paola Gramatica7, Yvonne C. Martin8, Roberto Todeschini9, Viviana Consonni9, Victor E. Kuz'min3, Richard Cramer10, Romualdo Benigni11, Chihae Yang12, James Rathman12,13, Lothar Terfloth14, Johann Gasteiger14, Ann Richard15, and Alexander Tropsha2,* 1Vancouver Prostate Centre, University of British Columbia, Vancouver, BC, V6H3Z6, Canada 2Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC, 27599, USA 3Department of Molecular Structure and Cheminformatics, A.V. Bogatsky Physical-Chemical Institute National Academy of Sciences of Ukraine, Odessa, 65080, Ukraine 4Department of Chemistry, L. Pasteur University of Strasbourg, Strasbourg, 67000, France 5Department of Physics, Lomonosov Moscow State University, Moscow, 119991, Russia 6School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool L33AF, UK 7Department of Structural and Functional Biology, University of Insubria, Varese, 21100, Italy 8Martin Consulting, Waukegan, IL, 60079, USA 9Milano Chemometrics and QSAR Research Group, University of Milano-Bicocca, Milan, 20126, Italy 10Tripos, Inc., St. Louis, MO, 63144, USA 11Environment and Health Department, Istituto Superiore di Sanita’, Rome, 00161, Italy 12Altamira LLC, Columbus OH 43235, USA 13Department of Chemical and Biomolecular Engineering, the Ohio State University, Columbus, OH 43215, USA 14Molecular Networks GmbH, 91052 Erlangen, Germany 15National Center for Computational Toxicology, U.S.
    [Show full text]
  • Chemoinformatics
    History and Challenges of Chemoinformatics Johann Gasteiger Computer-Chemie-Centrum University of Erlangen-Nürnberg D-91052 Erlangen, Germany www2.chemie.uni-erlangen.de/ C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture Overview • the scope of chemoinformatics • the beginnings • a field of ist own • scientific challenges • political challenges C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture Synthesis of Properties The most fundamental and lasting objective of synthesis is not production of new compounds but production of properties George S. Hammond Norris Award Lecture, 1968 C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture Fundamental Questions in Chemistry What structure do I need for a certain property? structure-activity relationships How do I make this structure? synthesis design What is the product of my reaction? reaction prediction structure elucidation C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture chemical physical property biological property property chemical structure synthesis reaction planning structure prediction elucidation starting materials C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture Chemoinformatics - Why? • complex relationships structure - biological activity chemical reactivity • amount of information many millions of compounds and reactions many millions of publications C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture Number of Compounds in Chemistry compounds published in CAS 50 40 30 20 10 compounds (millions) 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 year C3 © Gasteiger et al. Introduction into CI; SS 03/1st lecture From Data to Knowledge deductive inductive learning learning know- generalization ledge information context measurement data calculation C3 © Gasteiger et al.
    [Show full text]
  • Chemoinformatics
    Johann Gasteiger, Thomas Engel (Eds.) Chemoinformatics A Textbook Johann Gasteiger, Thomas Engel (Eds.) Chemoinformatics Related Titles from WILEY-VCH Hans-Dieter Höltje, Wolfgang Sippl, Didier Rognan, Gerd Folkers Molecular Modeling 2003 ISBN 3-527-30589-0 Helmut Gü nzler, Hans-Ulrich Gremlich IR Spectroscopy An Introduction 2002 ISBN 3-527-28896-1 Jure Zupan, Johann Gasteiger Neural Networks in Chemistry and Drug Design An Introduction 1999 ISBN 3-527-29778-2 (HC) ISBN 3-527-29779-0 (SC) Siegmar Braun, Hans-Otto Kalinowski, Stefan Berger 150 and More Basic NMR Experiments A Practical Course 1998 ISBN 3-527-29512-7 Johann Gasteiger, Thomas Engel (Eds.) Chemoinformatics A Textbook Editors: This book was carefully produced. Never- theless, editors, authors and publisher do Prof. Dr. Johann Gasteiger not warrant the information contained Computer-Chemie-Centrum and Institute therein to be free of errors. Readers are of Organic Chemistry advised to keep in mind that statements, University of Erlangen-Nü rnberg data, illustrations, procedural details or Nägelsbachstraû e 25 other items may inadvertently be inaccurate. 91052 Erlangen Germany Library of Congress Card No.: applied for A catalogue record for this book is available from the British Library. Dr. Thomas Engel Computer-Chemie-Centrum and Institute Bibliographic information published by of Organic Chemistry Die Deutsche Bibliothek University of Erlangen-Nürnberg Die Deutsche Bibliothek lists this publica- Nägelsbachstraû e 25 tion in the Deutsche Nationalbibliografie; 91052 Erlangen detailed bibliographic data is available in the Germany Internet at http://dnb.ddb.de ISBN 3-527-30681-1 c 2003 WILEY-VCH Verlag GmbH & Co. KgaA, Weinheim Printed on acid-free paper.
    [Show full text]
  • Applied Chemoinformatics
    Applied Chemoinformatics Applied Chemoinformatics Achievements and Future Opportunities Edited by Thomas Engel and Johann Gasteiger Editors All books published by Wiley-VCH are carefully produced. Nevertheless, Dr. Thomas Engel authors, editors, and publisher do not LMU Munich warrant the information contained in Department of Chemistry these books, including this book, to Butenandtstraße 5-13 be free of errors. Readers are advised 81377 München to keep in mind that statements, data, Germany illustrations, procedural details or other items may inadvertently be inaccurate. Prof. Dr. Johann Gasteiger Library of Congress Card No.: applied University of Erlangen-Nürnberg for Computer-Chemie-Centrum Nägelsbachstr. 25 British Library Cataloguing-in- 91052 Erlangen Publication Data Germany A catalogue record for this book is available from the British Library. Cover Design Dr. Christian R. Wick Bibliographic information published University of Erlangen-Nürnberg by the Deutsche Nationalbibliothek Institute for Theoretical Physics I The Deutsche Nationalbibliothek Nägelsbachstr. 49b (EAM) lists this publication in the Deutsche 91052 Erlangen Nationalbibliografie; detailed biblio- Germany graphic data are available on the Internet at <http://dnb.d-nb.de>. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form—by photoprinting, microfilm, or any other means—nor transmitted or translated into a machine
    [Show full text]
  • Chemoinformatics
    Chemoinformatics Chemoinformatics Basic Concepts and Methods Edited by Thomas Engel and Johann Gasteiger Editors All books published by Wiley-VCH are carefully produced. Nevertheless, authors, Dr. Thomas Engel editors, and publisher do not warrant the LMU München information contained in these books, Department of Chemistry including this book, to be free of errors. Butenandtstraße 5-13 Readers are advised to keep in mind that 81377 München statements, data, illustrations, procedural Germany details or other items may inadvertently be inaccurate. Prof. Dr. Johann Gasteiger Library of Congress Card No.: Universität Erlangen-Nürnberg applied for Computer-Chemie-Centrum Nägelsbachstr. 25 British Library Cataloguing-in-Publication 91052 Erlangen Data Germany A catalogue record for this book is avail- able from the British Library. Cover Design Dr. Christian R. Wick Bibliographic information published by University of Erlangen-Nürnberg the Deutsche Nationalbibliothek Institute for Theoretical Physics I The Deutsche Nationalbibliothek lists Nägelsbachstr. 49b (EAM) this publication in the Deutsche Nation- 91052 Erlangen albibliografie; detailed bibliographic Germany dataareavailableontheInternetat <http://dnb.d-nb.de>. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Boschstr. 12, 69469 Weinheim, Germany All rights reserved (including those of translation into other languages). No part of this book may be reproduced in any form – by photoprinting, microfilm, or any other means – nor transmitted or translated into a machine language with- out written permission
    [Show full text]
  • NEWSLETTER Summer 2018
    NEWSLETTER summer 2018 Above: Peter Willett (left) & Johann Gasteiger; see pages 12, 18 CICAG aims to keep its members abreast of the latest activities, services, and developments in all aspects of chemical information, from generation through to archiving, and in the computer applications used in this rapidly changing area through meetings, newsletters and professional networking. Chemical Information & Computer Applications Group Websites: http://www.rsc.org/CICAG https://www.liverpool.ac.uk/~ngberry/cicag/index.htm QR Code http://www.linkedin.com/groups?gid=1989945 MyRSC http://my.rsc.org/groups/cicag https://twitter.com/RSC_CICAG Table of Contents Chemical Information & Computer Applications Group Chair's Report 3 CICAG Planned and Proposed Future Meetings 3 The Formation of the CICAG 4 CICAG Committee News 5 Catalyst’s new gallery - Harry Baker and 120 years of Chlorine! 6 The 8th Capps Green Zomaya Award 7 Tony Kent Strix Award and Annual Lecture 2018 9 Undergraduate Research Bursaries – CICAG Sponsorship in 2018 11 Chemical Information / Cheminformatics and Related Books 12 Meeting Report: Dial-a-Molecule Conference 13 Meeting Report: ICCS 2018 14 Meeting Report: Artificial Intelligence in Chemistry Meeting 15 Professor Peter Willett – a Celebration of his Research 19 InfoChem News 20 CAS / SciFinder / STN News 21 Other Chemical Information Related News 23 Contributions to the CICAG Newsletter are welcome from all sources - please send to the Newsletter Editor: Stuart Newbold, email: [email protected] Chemical Information & Computer Applications Group Chair's Report Contributed by RSC CICAG Chair Dr Chris Swain, email: [email protected] This has been a busy few months for CICAG with a number of changes behind the scenes.
    [Show full text]
  • Once a Guest, Forever a Friend the TUM Research Alumni
    Once a Guest, Forever a Friend The TUM Research Alumni 1 2 Once a Guest, Forever a Friend The TUM Research Alumni We at TUM seek to invest in individuals with the passion to develop their talents, the freedom to transcend the boundaries between scientific disciplines and the courage to create new connections in knowledge and technology. Such are our research alumni. Without them TUM would not be the place that it is today. Wolfgang A. Herrmann President 3 We thank our partners Ideas Competition „Research Alumni of German Universities“ The ideas competition “Research Alumni of German Universities” was launched in 2011. It is part of the collaborative project “International Research Marketing”, which is a joint initiative by the Alexander von Humboldt Foundation (AvH), the German Academic Exchange Service (DAAD), the Deutsche Forschungsgemeinschaft (DFG) and the Fraunhofer- Gesellschaft. In 2012 the Technische Universität München was one of the award winners of the ideas competition. www.research-alumni.de Alexander von Humboldt Foundation Founded in 1953, the Alexander von Humboldt Foundation (AvH) supports science cooperation between outstanding researchers from Germany and abroad. Building on decades of experience in alumni networks and relations, in the last few years the AvH has focused on promoting research alumni activities at universities in Germany. www.humboldt-foundation.de Bund der Freunde der TUM e.V. The Bund der Freunde of TUM (BdF), founded in 1922, is the traditional association of sponsors and friends of the Technische Universität München. Its main goals are the promotion of junior scientists, the support of international cooperation and of alumni relations of the university.
    [Show full text]
  • Chemoinformatics: Achievements and Challenges, a Personal View
    molecules Review Chemoinformatics: Achievements and Challenges, a Personal View Johann Gasteiger Computer-Chemie-Centrum, University of Erlangen-Nuremberg, D-91052 Erlangen, Germany; [email protected]; Tel.: +49-89-795810 Academic Editor: Peter Willett Received: 20 November 2015 ; Accepted: 20 January 2016 ; Published: 27 January 2016 Abstract: Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society’s concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics.
    [Show full text]
  • Meanings of the Honor Award for Prof Kimito Funatsu
    Journal of Computer Aided Chemistry , Vol.20, 23-28 (2019) ISSN 1345-8647 23 (Mini-review) Meanings of the Honor Award for Prof Kimito Funatsu Manabu Sugimoto,a* Kenji Hori,b**, and Shigehiko Kanayac*** a Faculty of Advanced Science and Technology, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan b Division of Materials Science, Graduate School of Science and Technology for Innovation, Yamaguchi University, Tokiwadai, Ube 755-8611, Japan c Division of Information Science & Data Science Center, Graduate school of Science and Technology, Nara Institute of Science and Technology (NAIST), Takayama-cho 8916-5, Ikoma, Nara 630-0101, Japan (Received November 21, 2019; Accepted November 22, 2019) Prof. Kimito Funatsu received the Honor Award in Division of Chemoinformatics, the Chemical Society of Japan in the 42th Annual Meeting of Chemoinformatics held on Nov. 28th 2019. The awarding recognizes his significant contributions in the development of the cheminformatics discipline in the world as well as in Japan. His research efforts extend over multiple domains such as (i) system development including elucidation of chemical structures and prediction of organic reactions, (ii) quantitative structure activity relationship (QSAR), (iii) quantitative structure property relationship (QSPR), and (iv) international collaborations in chemoinformatics. In the present review, we focus on chemoinformatics in the world as well as in Japan based on “Special issue dedicating to Honor Award: Prof. Kimito Funatsu”, which consists of five invited papers by the world-famous distinguished foreign researchers, and six papers from domestic researchers. Taking these papers into consideration, we try to discuss the meanings of the Honor Award dedicating to Prof.
    [Show full text]
  • 1 Introduction
    1 1Introduction Thomas Engel1 and Johann Gasteiger2 1Ludwig-Maximilians-University Munich, Department of Chemistry, Butenandtstr. 5 - 13, 81377 Munich, Germany 2Computer-Chemie-Centrum, Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, Germany Outline 1.1 The Rationale for the Books, 1 1.2 Development of the Field, 2 1.3 The Basis of Chemoinformatics and the Diversity of Applications, 3 1.1 The Rationale for the Books In 2003 we issued the book Chemoinformatics: A Textbook (J. Gasteiger, T. Engel, Editors, Wiley-VCH Verlag GmbH, Weinheim, Germany, ISBN 13: 978-3-527-30681-7) which was well accepted and contributed to the development of the field of chemoinformatics. However, with the enormous progress in chemoinformatics, it is now time for an update. As we started out on this endeavor, it became rapidly clear that all the developments require presenting the field in more than a single book. We have therefore edited two volumes: • Chemoinformatics – Basic Concept and Methods [1] • Applied Chemoinformatics – Achievements and Future Opportunities In the first volume, “Basic Concept and Methods,” the essential foundations and methods that comprise the technology of chemoinformatics are presented. The links to this first volume are referenced in the present volume by“Methods Volume.” The Application Volume – tagged as shortcut in the first volume – emerged from the single “Applications” chapter of the 2003 textbook. The fact that appli- cations now merit a book of their own clearly demonstrates how enormously the Applied Chemoinformatics: Achievements and Future Opportunities, First Edition. Edited by Thomas Engel and Johann Gasteiger. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA. Published 2018 by Wiley-VCH Verlag GmbH & Co.
    [Show full text]