Literature DB >> 25647539

New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling.

Chihae Yang1,2,3, Aleksey Tarkhov1, Jörg Marusczyk1, Bruno Bienfait1, Johann Gasteiger1, Thomas Kleinoeder1, Tomasz Magdziarz1, Oliver Sacher1, Christof H Schwab1, Johannes Schwoebel1, Lothar Terfloth1, Kirk Arvidson3, Ann Richard4, Andrew Worth5, James Rathman2,6.   

Abstract

Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

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Year:  2015        PMID: 25647539     DOI: 10.1021/ci500667v

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  37 in total

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2.  Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways.

Authors:  Caroline L Ring; Jon A Arnot; Deborah H Bennett; Peter P Egeghy; Peter Fantke; Lei Huang; Kristin K Isaacs; Olivier Jolliet; Katherine A Phillips; Paul S Price; Hyeong-Moo Shin; John N Westgate; R Woodrow Setzer; John F Wambaugh
Journal:  Environ Sci Technol       Date:  2018-12-24       Impact factor: 9.028

3.  High-throughput screening of chemicals as functional substitutes using structure-based classification models.

Authors:  Katherine A Phillips; John F Wambaugh; Christopher M Grulke; Kathie L Dionisio; Kristin K Isaacs
Journal:  Green Chem       Date:  2017       Impact factor: 10.182

4.  Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity data.

Authors:  George Helman; Imran Shah; Grace Patlewicz
Journal:  Comput Toxicol       Date:  2019-11-01

5.  A systematic evaluation of analogs and automated read-across prediction of estrogenicity: A case study using hindered phenols.

Authors:  Prachi Pradeep; Kamel Mansouri; Grace Patlewicz; Richard Judson
Journal:  Comput Toxicol       Date:  2017-11-01

6.  Jmol SMILES and Jmol SMARTS: specifications and applications.

Authors:  Robert M Hanson
Journal:  J Cheminform       Date:  2016-09-26       Impact factor: 5.514

7.  The Tox21 10K Compound Library: Collaborative Chemistry Advancing Toxicology.

Authors:  Ann M Richard; Ruili Huang; Suramya Waidyanatha; Paul Shinn; Bradley J Collins; Inthirany Thillainadarajah; Christopher M Grulke; Antony J Williams; Ryan R Lougee; Richard S Judson; Keith A Houck; Mahmoud Shobair; Chihae Yang; James F Rathman; Adam Yasgar; Suzanne C Fitzpatrick; Anton Simeonov; Russell S Thomas; Kevin M Crofton; Richard S Paules; John R Bucher; Christopher P Austin; Robert J Kavlock; Raymond R Tice
Journal:  Chem Res Toxicol       Date:  2020-11-03       Impact factor: 3.739

8.  Exploring current read-across applications and needs among selected U.S. Federal Agencies.

Authors:  Grace Patlewicz; Lucina E Lizarraga; Diego Rua; David G Allen; Amber B Daniel; Suzanne C Fitzpatrick; Natàlia Garcia-Reyero; John Gordon; Pertti Hakkinen; Angela S Howard; Agnes Karmaus; Joanna Matheson; Moiz Mumtaz; Andrea-Nicole Richarz; Patricia Ruiz; Louis Scarano; Takashi Yamada; Nicole Kleinstreuer
Journal:  Regul Toxicol Pharmacol       Date:  2019-05-10       Impact factor: 3.271

9.  Integrated decision strategies for skin sensitization hazard.

Authors:  Judy Strickland; Qingda Zang; Nicole Kleinstreuer; Michael Paris; David M Lehmann; Neepa Choksi; Joanna Matheson; Abigail Jacobs; Anna Lowit; David Allen; Warren Casey
Journal:  J Appl Toxicol       Date:  2016-02-06       Impact factor: 3.446

10.  Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction.

Authors:  Daniel P Russo; Kimberley M Zorn; Alex M Clark; Hao Zhu; Sean Ekins
Journal:  Mol Pharm       Date:  2018-08-28       Impact factor: 4.939

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