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Nugmanov R. PaCh (Packed Chemicals): Computationally Effective Binary Format for Chemical Structure Encoding. J Chem Inf Model 2024; 64:3173-3179. [PMID: 38554112 DOI: 10.1021/acs.jcim.3c01720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/01/2024]
Abstract
In this work, we propose a versatile molecule and reaction encoding binary data format that aims to bridge the gap between the advantages of SMILES, like local stereo- and implicit hydrogen encoding, and block-structured MDL MOL with a 2D layout and explicit bond encoding, while addressing their respective limitations. Our new format introduces a balance between size efficiency, processing speed, and comprehensive representation, making it well-suited for various applications in cheminformatics, including deep learning, data storage, and searching. By offering an explicit approach to store atom connectivity (including implicit hydrogens), electronic state, stereochemistry, and other crucial molecular attributes, our proposal seeks to enhance data storage efficiency and promote interoperability among different software tools.
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Affiliation(s)
- Ramil Nugmanov
- Alexander Butlerov Institute of Chemistry, Kazan Federal University, 18 Kremlevskaya Street, Kazan 420008, Russia
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2
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Vaitkus A, Merkys A, Sander T, Quirós M, Thiessen PA, Bolton EE, Gražulis S. A workflow for deriving chemical entities from crystallographic data and its application to the Crystallography Open Database. J Cheminform 2023; 15:123. [PMID: 38115123 PMCID: PMC10730636 DOI: 10.1186/s13321-023-00780-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 11/09/2023] [Indexed: 12/21/2023] Open
Abstract
Knowledge about the 3-dimensional structure, orientation and interaction of chemical compounds is important in many areas of science and technology. X-ray crystallography is one of the experimental techniques capable of providing a large amount of structural information for a given compound, and it is widely used for characterisation of organic and metal-organic molecules. The method provides precise 3D coordinates of atoms inside crystals, however, it does not directly deliver information about certain chemical characteristics such as bond orders, delocalization, charges, lone electron pairs or lone electrons. These aspects of a molecular model have to be derived from crystallographic data using refined information about interatomic distances and atom types as well as employing general chemical knowledge. This publication describes a curated automatic pipeline for the derivation of chemical attributes of molecules from crystallographic models. The method is applied to build a catalogue of chemical entities in an open-access crystallographic database, the Crystallography Open Database (COD). The catalogue of such chemical entities is provided openly as a derived database. The content of this catalogue and the problems arising in the fully automated pipeline are discussed, along with the possibilities to introduce manual data curation into the process.
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Affiliation(s)
- Antanas Vaitkus
- Section of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio al. 7, Vilnius, LT-10257, Lithuania.
| | - Andrius Merkys
- Section of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio al. 7, Vilnius, LT-10257, Lithuania
| | - Thomas Sander
- Scientific Computing Drug Discovery, Idorsia Pharmaceuticals Ltd., Hegenheimermattweg 89, Allschwil, 4123, Switzerland
| | - Miguel Quirós
- Departamento de Química Inorgánica, Universidad de Granada, Granada, 18071, Spain
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Saulius Gražulis
- Section of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Saulėtekio al. 7, Vilnius, LT-10257, Lithuania
- Faculty of Mathematics and Informatics, Vilnius University, Naugarduko g. 24, Vilnius, LT-03225, Lithuania
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3
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Merkys A, Vaitkus A, Grybauskas A, Konovalovas A, Quirós M, Gražulis S. Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions. J Cheminform 2023; 15:25. [PMID: 36814296 PMCID: PMC9948373 DOI: 10.1186/s13321-023-00692-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Accepted: 02/02/2023] [Indexed: 02/24/2023] Open
Abstract
Published reports of chemical compounds often contain multiple machine-readable descriptions which may supplement each other in order to yield coherent and complete chemical representations. This publication presents a method to cross-check such descriptions using a canonical representation and isomorphism of molecular graphs. If immediate agreement between compound descriptions is not found, the algorithm derives the minimal set of simplifications required for both descriptions to arrive to a matching form (if any). The proposed algorithm is used to cross-check chemical descriptions from the Crystallography Open Database to identify coherently described entries as well as those requiring further curation.
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Affiliation(s)
- Andrius Merkys
- Sector of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania.
| | - Antanas Vaitkus
- grid.6441.70000 0001 2243 2806Sector of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Algirdas Grybauskas
- grid.6441.70000 0001 2243 2806Sector of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Aleksandras Konovalovas
- grid.6441.70000 0001 2243 2806Department of Biochemistry and Molecular Biology, Institute of Biosciences, Life Sciences Center, Vilnius University, Vilnius, Lithuania
| | - Miguel Quirós
- grid.4489.10000000121678994Departamento de Química Inorgánica, Universidad de Granada, 18071 Granada, Spain
| | - Saulius Gražulis
- grid.6441.70000 0001 2243 2806Sector of Crystallography and Chemical Informatics, Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius, Lithuania
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4
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Krenn M, Ai Q, Barthel S, Carson N, Frei A, Frey NC, Friederich P, Gaudin T, Gayle AA, Jablonka KM, Lameiro RF, Lemm D, Lo A, Moosavi SM, Nápoles-Duarte JM, Nigam A, Pollice R, Rajan K, Schatzschneider U, Schwaller P, Skreta M, Smit B, Strieth-Kalthoff F, Sun C, Tom G, Falk von Rudorff G, Wang A, White AD, Young A, Yu R, Aspuru-Guzik A. SELFIES and the future of molecular string representations. PATTERNS (NEW YORK, N.Y.) 2022; 3:100588. [PMID: 36277819 PMCID: PMC9583042 DOI: 10.1016/j.patter.2022.100588] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways, or the design of new molecules. The machine needs to read and write fluently in a chemical language for each of these tasks. Strings are a common tool to represent molecular graphs, and the most popular molecular string representation, Smiles, has powered cheminformatics since the late 1980s. However, in the context of AI and ML in chemistry, Smiles has several shortcomings-most pertinently, most combinations of symbols lead to invalid results with no valid chemical interpretation. To overcome this issue, a new language for molecules was introduced in 2020 that guarantees 100% robustness: SELF-referencing embedded string (Selfies). Selfies has since simplified and enabled numerous new applications in chemistry. In this perspective, we look to the future and discuss molecular string representations, along with their respective opportunities and challenges. We propose 16 concrete future projects for robust molecular representations. These involve the extension toward new chemical domains, exciting questions at the interface of AI and robust languages, and interpretability for both humans and machines. We hope that these proposals will inspire several follow-up works exploiting the full potential of molecular string representations for the future of AI in chemistry and materials science.
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Affiliation(s)
- Mario Krenn
- Max Planck Institute for the Science of Light (MPL), Erlangen, Germany,Corresponding author
| | - Qianxiang Ai
- Department of Chemistry, Fordham University, The Bronx, NY, USA
| | - Senja Barthel
- Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nessa Carson
- Syngenta Jealott’s Hill International Research Centre, Bracknell, Berkshire, UK
| | - Angelo Frei
- Department of Chemistry, Imperial College London, Molecular Sciences Research Hub, White City Campus, Wood Lane, London, UK
| | - Nathan C. Frey
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany,Institute of Nanotechnology, Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany
| | - Théophile Gaudin
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,IBM Research Europe, Zürich, Switzerland
| | | | - Kevin Maik Jablonka
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Rafael F. Lameiro
- Medicinal and Biological Chemistry Group, São Carlos Institute of Chemistry, University of São Paulo, São Paulo, Brazil
| | - Dominik Lemm
- Faculty of Physics, University of Vienna, Vienna, Austria
| | - Alston Lo
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Seyed Mohamad Moosavi
- Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany
| | | | - AkshatKumar Nigam
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Kohulan Rajan
- Institute for Inorganic and Analytical Chemistry, Friedrich-Schiller Universität Jena, Jena, Germany
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Würzburg, Germany
| | - Philippe Schwaller
- IBM Research Europe, Zürich, Switzerland,Laboratory of Artificial Chemical Intelligence (LIAC), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland,National Centre of Competence in Research (NCCR) Catalysis, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marta Skreta
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), Sion, Valais, Switzerland
| | - Felix Strieth-Kalthoff
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Chong Sun
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
| | - Gary Tom
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | | | - Andrew Wang
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada,Solar Fuels Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada
| | - Andrew D. White
- Department of Chemical Engineering, University of Rochester, Rochester, NY, USA
| | - Adamo Young
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,Vector Institute for Artificial Intelligence, Toronto, ON, Canada
| | - Rose Yu
- Department of Computer Science and Engineering, University of California, San Diego, La Jolla, CA, USA
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Toronto, ON, Canada,Chemical Physics Theory Group, Department of Chemistry, University of Toronto, Toronto, ON, Canada,Vector Institute for Artificial Intelligence, Toronto, ON, Canada,Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON, Canada,Department of Materials Science, University of Toronto, Toronto, ON, Canada,Canadian Institute for Advanced Research (CIFAR) Lebovic Fellow, Toronto, ON, Canada,Corresponding author
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5
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Brammer JC, Blanke G, Kellner C, Hoffmann A, Herres-Pawlis S, Schatzschneider U. TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson. J Cheminform 2022; 14:66. [PMID: 36171627 PMCID: PMC9520877 DOI: 10.1186/s13321-022-00640-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 08/14/2022] [Indexed: 11/10/2022] Open
Abstract
TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical “tuple-style” output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation.
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Affiliation(s)
- Jan C Brammer
- Institut für Anorganische Chemie, RWTH Aachen, Landoltweg 1a, 52074, Aachen, Germany
| | - Gerd Blanke
- StructurePendium Technologies GmbH, Reulsbergweg 5, 45257, Essen, Germany
| | - Claudia Kellner
- Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074, Würzburg, Germany
| | - Alexander Hoffmann
- Institut für Anorganische Chemie, RWTH Aachen, Landoltweg 1a, 52074, Aachen, Germany
| | - Sonja Herres-Pawlis
- Institut für Anorganische Chemie, RWTH Aachen, Landoltweg 1a, 52074, Aachen, Germany.
| | - Ulrich Schatzschneider
- Institut für Anorganische Chemie, Julius-Maximilians-Universität Würzburg, Am Hubland, 97074, Würzburg, Germany.
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6
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Wigh DS, Goodman JM, Lapkin AA. A review of molecular representation in the age of machine learning. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Affiliation(s)
- Daniel S. Wigh
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | | | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
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7
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Vogiatzis KD, Polynski MV, Kirkland JK, Townsend J, Hashemi A, Liu C, Pidko EA. Computational Approach to Molecular Catalysis by 3d Transition Metals: Challenges and Opportunities. Chem Rev 2019; 119:2453-2523. [PMID: 30376310 PMCID: PMC6396130 DOI: 10.1021/acs.chemrev.8b00361] [Citation(s) in RCA: 214] [Impact Index Per Article: 42.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Indexed: 12/28/2022]
Abstract
Computational chemistry provides a versatile toolbox for studying mechanistic details of catalytic reactions and holds promise to deliver practical strategies to enable the rational in silico catalyst design. The versatile reactivity and nontrivial electronic structure effects, common for systems based on 3d transition metals, introduce additional complexity that may represent a particular challenge to the standard computational strategies. In this review, we discuss the challenges and capabilities of modern electronic structure methods for studying the reaction mechanisms promoted by 3d transition metal molecular catalysts. Particular focus will be placed on the ways of addressing the multiconfigurational problem in electronic structure calculations and the role of expert bias in the practical utilization of the available methods. The development of density functionals designed to address transition metals is also discussed. Special emphasis is placed on the methods that account for solvation effects and the multicomponent nature of practical catalytic systems. This is followed by an overview of recent computational studies addressing the mechanistic complexity of catalytic processes by molecular catalysts based on 3d metals. Cases that involve noninnocent ligands, multicomponent reaction systems, metal-ligand and metal-metal cooperativity, as well as modeling complex catalytic systems such as metal-organic frameworks are presented. Conventionally, computational studies on catalytic mechanisms are heavily dependent on the chemical intuition and expert input of the researcher. Recent developments in advanced automated methods for reaction path analysis hold promise for eliminating such human-bias from computational catalysis studies. A brief overview of these approaches is presented in the final section of the review. The paper is closed with general concluding remarks.
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Affiliation(s)
| | | | - Justin K. Kirkland
- Department
of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Jacob Townsend
- Department
of Chemistry, University of Tennessee, Knoxville, Tennessee 37996, United States
| | - Ali Hashemi
- Inorganic
Systems Engineering group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Chong Liu
- Inorganic
Systems Engineering group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands
| | - Evgeny A. Pidko
- TheoMAT
group, ITMO University, Lomonosova 9, St. Petersburg 191002, Russia
- Inorganic
Systems Engineering group, Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, 2629 HZ Delft, The Netherlands
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8
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Grajciar L, Heard CJ, Bondarenko AA, Polynski MV, Meeprasert J, Pidko EA, Nachtigall P. Towards operando computational modeling in heterogeneous catalysis. Chem Soc Rev 2018; 47:8307-8348. [PMID: 30204184 PMCID: PMC6240816 DOI: 10.1039/c8cs00398j] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Indexed: 12/19/2022]
Abstract
An increased synergy between experimental and theoretical investigations in heterogeneous catalysis has become apparent during the last decade. Experimental work has extended from ultra-high vacuum and low temperature towards operando conditions. These developments have motivated the computational community to move from standard descriptive computational models, based on inspection of the potential energy surface at 0 K and low reactant concentrations (0 K/UHV model), to more realistic conditions. The transition from 0 K/UHV to operando models has been backed by significant developments in computer hardware and software over the past few decades. New methodological developments, designed to overcome part of the gap between 0 K/UHV and operando conditions, include (i) global optimization techniques, (ii) ab initio constrained thermodynamics, (iii) biased molecular dynamics, (iv) microkinetic models of reaction networks and (v) machine learning approaches. The importance of the transition is highlighted by discussing how the molecular level picture of catalytic sites and the associated reaction mechanisms changes when the chemical environment, pressure and temperature effects are correctly accounted for in molecular simulations. It is the purpose of this review to discuss each method on an equal footing, and to draw connections between methods, particularly where they may be applied in combination.
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Affiliation(s)
- Lukáš Grajciar
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Christopher J. Heard
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
| | - Anton A. Bondarenko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Mikhail V. Polynski
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
| | - Jittima Meeprasert
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Evgeny A. Pidko
- TheoMAT group
, ITMO University
,
Lomonosova 9
, St. Petersburg
, 191002
, Russia
- Inorganic Systems Engineering group
, Department of Chemical Engineering
, Faculty of Applied Sciences
, Delft University of Technology
,
Van der Maasweg 9
, 2629 HZ Delft
, The Netherlands
.
| | - Petr Nachtigall
- Department of Physical and Macromolecular Chemistry
, Faculty of Science
, Charles University in Prague
,
128 43 Prague 2
, Czech Republic
.
;
;
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9
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Hähnke VD, Kim S, Bolton EE. PubChem chemical structure standardization. J Cheminform 2018; 10:36. [PMID: 30097821 PMCID: PMC6086778 DOI: 10.1186/s13321-018-0293-8] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 08/01/2018] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND PubChem is a chemical information repository, consisting of three primary databases: Substance, Compound, and BioAssay. When individual data contributors submit chemical substance descriptions to Substance, the unique chemical structures are extracted and stored into Compound through an automated process called structure standardization. The present study describes the PubChem standardization approaches and analyzes them for their success rates, reasons that cause structures to be rejected, and modifications applied to structures during the standardization process. Furthermore, the PubChem standardization is compared to the structure normalization of the IUPAC International Chemical Identifier (InChI) software, as manifested by conversion of the InChI back into a chemical structure. RESULTS The observed rejection rate for substances processed by PubChem standardization was 0.36%, which is predominantly attributed to structures with invalid atom valences that cannot be readily corrected without additional information from contributors. Of all structures that pass standardization, 44% are modified in the process, reducing the count of unique structures from 53,574,724 in substance to 45,808,881 in compound as identified by de-aromatized canonical isomeric SMILES. Even though the processing time is very low on average (only 0.4% of structures have individual standardization time above 0.1 s), total standardization time is completely dominated by edge cases: 90% of the time to standardize all structures in PubChem substance is spent on the 2.05% of structures with the highest individual standardization time. It is worth noting that 60% of the structures obtained from PubChem structure standardization are not identical to the chemical structure resulting from the InChI (primarily due to preferences for a different tautomeric form). CONCLUSIONS Standardization of chemical structures is complicated by the diversity of chemical information and their representations approaches. The PubChem standardization is an effective and efficient tool to account for molecular diversity and to eliminate invalid/incomplete structures. Further development will concentrate on improved tautomer consideration and an expanded stereocenter definition. Modifications are difficult to thoroughly validate, with slight changes often affecting many thousands of structures and various edge cases. The PubChem structure standardization service is accessible as a public resource ( https://pubchem.ncbi.nlm.nih.gov/standardize ), and via programmatic interfaces.
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Affiliation(s)
- Volker D. Hähnke
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
- Present Address: European Patent Office, Patentlaan 2, 2288 EE Rijswijk, The Netherlands
| | - Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Department of Health and Human Services, 8600 Rockville Pike, Bethesda, MD 20894 USA
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10
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Jarvis J, Seed MJ, Stocks SJ, Agius RM. A refined QSAR model for prediction of chemical asthma hazard. Occup Med (Lond) 2015. [PMID: 26209225 DOI: 10.1093/occmed/kqv105] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND A previously developed quantitative structure-activity relationship (QSAR) model has been extern ally validated as a good predictor of chemical asthma hazard (sensitivity: 79-86%, specificity: 93-99%). AIMS To develop and validate a second version of this model. METHODS Learning dataset asthmagenic chemicals with molecular weight (MW) <1 kDa were identified from reports published in the peer-reviewed literature before the end of 2012. Control chemicals for which no reported case(s) of occupational asthma had been identified were selected at random from UK and US occupational exposure limit tables. MW banding was used in an attempt to categorically match the control group for MW distribution of the asthmagens. About 10% of chemicals in each MW category were excluded for use as an external validation set. An independent researcher utilized a logistic regression approach to compare the molecular descriptors present in asthmagens and controls. The resulting equation generated a hazard index (HI), with a value between zero and one, as an estimate of the probability that the chemical had asthmagenic potential. The HI was determined for each compound in the external validation set. RESULTS The model development sets comprised 99 chemical asthmagens and 204 controls. The external validation showed that using a cut-point HI of 0.39, 9/10 asthmagenic (sensitivity: 90%) and 23/24 non-asthmagenic (specificity: 96%) compounds were correctly predicted. The new QSAR model showed a better receiver operating characteristic plot than the original. CONCLUSIONS QSAR refinement by iteration has resulted in an improved model for the prediction of chemical asthma hazard.
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Affiliation(s)
- J Jarvis
- Department of Information Services, The University of Edinburgh, Edinburgh EH8 9LJ, UK
| | - M J Seed
- Centre for Occupational and Environmental Health, Centre for Epidemiology, Institute of Population Health, Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PL, UK.
| | - S J Stocks
- Centre for Occupational and Environmental Health, Centre for Epidemiology, Institute of Population Health, Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PL, UK
| | - R M Agius
- Centre for Occupational and Environmental Health, Centre for Epidemiology, Institute of Population Health, Faculty of Medical and Human Sciences, The University of Manchester, Manchester M13 9PL, UK
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11
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Clark AM, Williams AJ, Ekins S. Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data. J Cheminform 2015; 7:9. [PMID: 25798198 PMCID: PMC4369291 DOI: 10.1186/s13321-015-0057-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2014] [Accepted: 02/23/2015] [Indexed: 11/12/2022] Open
Abstract
The current rise in the use of open lab notebook techniques means that there are an increasing number of scientists who make chemical information freely and openly available to the entire community as a series of micropublications that are released shortly after the conclusion of each experiment. We propose that this trend be accompanied by a thorough examination of data sharing priorities. We argue that the most significant immediate benefactor of open data is in fact chemical algorithms, which are capable of absorbing vast quantities of data, and using it to present concise insights to working chemists, on a scale that could not be achieved by traditional publication methods. Making this goal practically achievable will require a paradigm shift in the way individual scientists translate their data into digital form, since most contemporary methods of data entry are designed for presentation to humans rather than consumption by machine learning algorithms. We discuss some of the complex issues involved in fixing current methods, as well as some of the immediate benefits that can be gained when open data is published correctly using unambiguous machine readable formats. Lab notebook entries must target both visualisation by scientists and use by machine learning algorithms ![]()
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, H3J 2S1, QC Canada
| | - Antony J Williams
- Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, NC 27587 USA
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC 27526 USA ; Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010 USA
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Clark AM, Sarker M, Ekins S. New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0. J Cheminform 2014; 6:38. [PMID: 25302078 PMCID: PMC4190048 DOI: 10.1186/s13321-014-0038-2] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 06/30/2014] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND We recently developed a freely available mobile app (TB Mobile) for both iOS and Android platforms that displays Mycobacterium tuberculosis (Mtb) active molecule structures and their targets with links to associated data. The app was developed to make target information available to as large an audience as possible. RESULTS We now report a major update of the iOS version of the app. This includes enhancements that use an implementation of ECFP_6 fingerprints that we have made open source. Using these fingerprints, the user can propose compounds with possible anti-TB activity, and view the compounds within a cluster landscape. Proposed compounds can also be compared to existing target data, using a näive Bayesian scoring system to rank probable targets. We have curated an additional 60 new compounds and their targets for Mtb and added these to the original set of 745 compounds. We have also curated 20 further compounds (many without targets in TB Mobile) to evaluate this version of the app with 805 compounds and associated targets. CONCLUSIONS TB Mobile can now manage a small collection of compounds that can be imported from external sources, or exported by various means such as email or app-to-app inter-process communication. This means that TB Mobile can be used as a node within a growing ecosystem of mobile apps for cheminformatics. It can also cluster compounds and use internal algorithms to help identify potential targets based on molecular similarity. TB Mobile represents a valuable dataset, data-visualization aid and target prediction tool.
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, 1900 St. Jacques #302, Montreal H3J 2S1, Quebec, Canada
| | - Malabika Sarker
- SRI International, 333 Ravenswood Avenue, Menlo Park 94025, CA, USA
| | - Sean Ekins
- Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame 94010, CA, USA
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina 27526, NC, USA
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Clark AM. Rendering Molecular Sketches for Publication Quality Output. Mol Inform 2013; 32:291-301. [PMID: 27481524 DOI: 10.1002/minf.201200171] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2012] [Accepted: 02/15/2013] [Indexed: 11/11/2022]
Abstract
The creation of 2D molecular structure diagrams that make full use of the capabilities of modern display systems, using only input data expressed in file formats used for cheminformatics, is a complex task that requires a number of additional algorithms. Assuming that atom positions have been well chosen, the rendering engine is required to micromanage the precise positioning of atom labels, bonds and atom adjuncts, in such a way that the final output is correct, consistent with convention, and as pleasing to the eye as a diagram produced by a graphic designer. The techniques must be equally applicable when creating output for low-resolution screens and high resolution printed output, and make use of contemporary graphics file formats in such a way that the largest possible number of software platforms are able to display the output at any resolution without degradation or inconsistency. The main issues involved in meeting these criteria are discussed, and algorithms for satisfying them are presented.
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, 1900 St. Jacques #302, H3J2S1 Montreal, Quebec,CA phone: 5148672328.
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Clark AM, Ekins S, Williams AJ. Redefining Cheminformatics with Intuitive Collaborative Mobile Apps. Mol Inform 2012; 31:569-584. [PMID: 23198002 PMCID: PMC3503261 DOI: 10.1002/minf.201200010] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2012] [Accepted: 04/21/2012] [Indexed: 11/11/2022]
Abstract
The proliferation of mobile devices such as smartphones and tablet computers has recently been extended to include a growing ecosystem of increasingly sophisticated chemistry software packages, commonly known as apps. The capabilities that these apps can offer to the practicing chemist are approaching those of conventional desktop-based software, but apps tend to be focused on a relatively small range of tasks. To overcome this, chemistry apps must be able to seamlessly transfer data to other apps, and through the network to other devices, as well as to other platforms, such as desktops and servers, using documented file formats and protocols whenever possible. This article describes the development and state of the art with regard to chemistry-aware apps that make use of facile data interchange, and some of the scenarios in which these apps can be inserted into a chemical information workflow to increase productivity. A selection of contemporary apps is used to demonstrate their relevance to pharmaceutical research. Mobile apps represent a novel approach for delivery of cheminformatics tools to chemists and other scientists, and indications suggest that mobile devices represent a disruptive technology for drug discovery, as they have been to many other industries.
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Affiliation(s)
- Alex M Clark
- Molecular Materials Informatics, 1900 St. Jacques #302, Montreal, Quebec, Canada H3J 2S1
| | - Sean Ekins
- Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay Varina, NC 27526, USA
| | - Antony J Williams
- Royal Society of Chemistry, 904 Tamaras Circle, Wake Forest, NC-27587, USA
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