Literature DB >> 28316648

Jmol SMILES and Jmol SMARTS: specifications and applications.

Robert M Hanson1.   

Abstract

<n class="Chemical">sppan>an class="abstract_title">BACKGROUND: SMILES and SMARTS are two well-defined structure matching languages that have gained wide use in cheminformatics. Jmol is a widely used open-source molecular visualization and analy<span class="Chemical">sis tool written in Java and implemented in both Java and JavaScript. Over the past 10 years, from 2007 to 2016, work on Jmol has included the development of dialects of SMILES and SMARTS that incorporate novel aspects that allow new and powerful applications.
RESULTS: The specifications of "Jmol SMILES" and "Jmol SMARTS" are described. The dialects most closely resemble OpenSMILES and OpenSMARTS. Jmol SMILES is a superset of OpenSMILES, allowing a freer format, including whitespace and comments, the addition of "processing directives" that modify the meaning of certain aspects of SMILES processing such as aromaticity and stereochemistry, a more extensive treatment of stereochemistry, and several minor additions. Jmol SMARTS similarly adds these same modifications to OpenSMARTS, but also adds a number of additional "primitives" and elements of syntax tuned to matching 3D molecular structures and selecting their atoms. The result is an expansion of the capabilities of SMILES and SMARTS primarily for use in 3D molecular analysis, allowing a broader range of matching involving any combination of 3D molecular structures, SMILES strings, and SMARTS patterns. While developed specifically for Jmol, these dialects of SMILES and SMARTS are independent of the Jmol application itself.
CONCLUSIONS: Jmol SMILES and Jmol SMARTS add value to standard SMILES and SMARTS. Together they have proven exceptionally capable in extracting valuable information from 3D structural models, as demonstrated in Jmol. Capabilities in Jmol enabled by Jmol SMILES and Jmol SMARTS include efficient MMFF94 atom typing, conformational identification, SMILES comparisons without canonicalization, identification of stereochemical relationships, quantitative comparison of 3D structures from different sources (including differences in Kekulization), conformational flexible fitting, and atom mapping used to synchronize interactive displays of 2D structures, 3D structures, and spectral correlations, where data are being drawn from multiple sources.

Entities:  

Year:  2016        PMID: 28316648      PMCID: PMC5037863          DOI: 10.1186/s13321-016-0160-4

Source DB:  PubMed          Journal:  J Cheminform        ISSN: 1758-2946            Impact factor:   5.514


  9 in total

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Authors:  Chihae Yang; Aleksey Tarkhov; Jörg Marusczyk; Bruno Bienfait; Johann Gasteiger; Thomas Kleinoeder; Tomasz Magdziarz; Oliver Sacher; Christof H Schwab; Johannes Schwoebel; Lothar Terfloth; Kirk Arvidson; Ann Richard; Andrew Worth; James Rathman
Journal:  J Chem Inf Model       Date:  2015-02-19       Impact factor: 4.956

3.  Molecular query language (MQL)--a context-free grammar for substructure matching.

Authors:  Ewgenij Proschak; Jörg K Wegner; Andreas Schüller; Gisbert Schneider; Uli Fechner
Journal:  J Chem Inf Model       Date:  2007 Mar-Apr       Impact factor: 4.956

4.  SYBYL line notation (SLN): a single notation to represent chemical structures, queries, reactions, and virtual libraries.

Authors:  R Webster Homer; Jon Swanson; Robert J Jilek; Tad Hurst; Robert D Clark
Journal:  J Chem Inf Model       Date:  2008-12       Impact factor: 4.956

5.  VMD: visual molecular dynamics.

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Journal:  J Mol Graph       Date:  1996-02

6.  CHORTLES: a method for representing oligomeric and template-based mixtures.

Authors:  M A Siani; D Weininger; C A James; J M Blaney
Journal:  J Chem Inf Comput Sci       Date:  1995 Nov-Dec

7.  CHUCKLES: a method for representing and searching peptide and peptoid sequences on both monomer and atomic levels.

Authors:  M A Siani; D Weininger; J M Blaney
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8.  CurlySMILES: a chemical language to customize and annotate encodings of molecular and nanodevice structures.

Authors:  Axel Drefahl
Journal:  J Cheminform       Date:  2011-01-07       Impact factor: 5.514

9.  JSME: a free molecule editor in JavaScript.

Authors:  Bruno Bienfait; Peter Ertl
Journal:  J Cheminform       Date:  2013-05-21       Impact factor: 5.514

  9 in total
  8 in total

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Authors:  Ayaz Hassan; Lucyano J A Macedo; João C P de Souza; Filipe C D A Lima; Frank N Crespilho
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3.  Prediction Model with High-Performance Constitutive Androstane Receptor (CAR) Using DeepSnap-Deep Learning Approach from the Tox21 10K Compound Library.

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4.  DeepSnap-Deep Learning Approach Predicts Progesterone Receptor Antagonist Activity With High Performance.

Authors:  Yasunari Matsuzaka; Yoshihiro Uesawa
Journal:  Front Bioeng Biotechnol       Date:  2020-01-22

5.  Synthesis, molecular docking, and in silico ADMET studies of 4-benzyl-1-(2,4,6-trimethyl-benzyl)-piperidine: Potential Inhibitor of SARS-CoV2.

Authors:  R Nandini Asha; B Ravindran Durai Nayagam; Nattamai Bhuvanesh
Journal:  Bioorg Chem       Date:  2021-05-05       Impact factor: 5.275

6.  A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance.

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7.  SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

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Review 8.  Review of techniques and models used in optical chemical structure recognition in images and scanned documents.

Authors:  Fidan Musazade; Narmin Jamalova; Jamaladdin Hasanov
Journal:  J Cheminform       Date:  2022-09-09       Impact factor: 8.489

  8 in total

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