Literature DB >> 18998666

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

R Webster Homer1, Jon Swanson, Robert J Jilek, Tad Hurst, Robert D Clark.   

Abstract

SYBYL line notation (SLN) is a powerful way to represent molecular structures, reactions, libraries of structures, molecular fragments, formulations, molecular queries, and reaction queries. Nearly any chemical structure imaginable, including macromolecules, pharmaceuticals, catalysts, and even combinatorial libraries can be represented as an SLN string. The language provides a rich syntax for database queries comparable to SMARTS. It provides full Markush, R-Group, reaction, and macro atom capabilities in a single unified notation. It includes the ability to specify 3D conformations and 2D depictions. All the information necessary to recreate the structure in a modeling or drawing package is present in a single, concise string of ASCII characters. This makes SLN ideal for structure communication over global computer networks between applications sitting at remote sites. Unlike SMILES and its derivatives, SLN accomplishes this within a single unified syntax. Structures, queries, compounds, reactions, and virtual libraries can all be represented in a single notation.

Year:  2008        PMID: 18998666     DOI: 10.1021/ci7004687

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


  26 in total

Review 1.  Common cases of improper lipid annotation using high-resolution tandem mass spectrometry data and corresponding limitations in biological interpretation.

Authors:  Jeremy P Koelmel; Candice Z Ulmer; Christina M Jones; Richard A Yost; John A Bowden
Journal:  Biochim Biophys Acta Mol Cell Biol Lipids       Date:  2017-03-02       Impact factor: 4.698

2.  A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers.

Authors:  Roman J Dorfman; Karl M Smith; Brian B Masek; Robert D Clark
Journal:  J Comput Aided Mol Des       Date:  2007-12-06       Impact factor: 3.686

3.  Many InChIs and quite some feat.

Authors:  Wendy A Warr
Journal:  J Comput Aided Mol Des       Date:  2015-06-17       Impact factor: 3.686

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

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

5.  Discovery of novel bacterial elongation condensing enzyme inhibitors by virtual screening.

Authors:  Zhong Zheng; Joshua B Parsons; Rajendra Tangallapally; Weixing Zhang; Charles O Rock; Richard E Lee
Journal:  Bioorg Med Chem Lett       Date:  2014-04-02       Impact factor: 2.823

6.  Alchemical Binding Free Energy Calculations in AMBER20: Advances and Best Practices for Drug Discovery.

Authors:  Tai-Sung Lee; Bryce K Allen; Timothy J Giese; Zhenyu Guo; Pengfei Li; Charles Lin; T Dwight McGee; David A Pearlman; Brian K Radak; Yujun Tao; Hsu-Chun Tsai; Huafeng Xu; Woody Sherman; Darrin M York
Journal:  J Chem Inf Model       Date:  2020-09-16       Impact factor: 4.956

7.  A cell-based screening system for RNA polymerase I inhibitors.

Authors:  Xiao Tan; Samuel G Awuah
Journal:  Medchemcomm       Date:  2019-07-17       Impact factor: 3.597

8.  Incorporation of non-natural amino acids improves cell permeability and potency of specific inhibitors of proteasome trypsin-like sites.

Authors:  Paul P Geurink; Wouter A van der Linden; Anne C Mirabella; Nerea Gallastegui; Gerjan de Bruin; Annet E M Blom; Mathias J Voges; Elliot D Mock; Bogdan I Florea; Gijs A van der Marel; Christoph Driessen; Mario van der Stelt; Michael Groll; Herman S Overkleeft; Alexei F Kisselev
Journal:  J Med Chem       Date:  2013-01-28       Impact factor: 7.446

9.  Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI.

Authors:  Noel M O'Boyle
Journal:  J Cheminform       Date:  2012-09-18       Impact factor: 5.514

10.  Toward the discovery of vaccine adjuvants: coupling in silico screening and in vitro analysis of antagonist binding to human and mouse CCR4 receptors.

Authors:  Matthew N Davies; Jagadeesh Bayry; Elma Z Tchilian; Janakiraman Vani; Melkote S Shaila; Emily K Forbes; Simon J Draper; Peter C L Beverley; David F Tough; Darren R Flower
Journal:  PLoS One       Date:  2009-11-30       Impact factor: 3.240

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