Literature DB >> 22482697

New benchmark for chemical nomenclature software.

Edward O Cannon1.   

Abstract

We propose a new, robust benchmark, called Percentage Round Tripping of Canonical Isomeric SMILES (%RTCS), for assessing the ability of chemical nomenclature software to convert chemical structures to names and chemical names to structures. The benchmark is based on a string comparison between canonical isomeric SMILES generated from the original structure and the resultant structure from round tripping. Using the latest version of the OpenEye chemical nomenclature toolkit, Lexichem v2.1.0, we report %RTCS values of over 92% on average for a variety of challenging compound collections.

Year:  2012        PMID: 22482697     DOI: 10.1021/ci3000419

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


  6 in total

1.  Calculating Partition Coefficients of Small Molecules in Octanol/Water and Cyclohexane/Water.

Authors:  Caitlin C Bannan; Gaetano Calabró; Daisy Y Kyu; David L Mobley
Journal:  J Chem Theory Comput       Date:  2016-08-01       Impact factor: 6.006

2.  Many InChIs and quite some feat.

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

3.  Contemporary Computational Applications and Tools in Drug Discovery.

Authors:  Philip B Cox; Rishi Gupta
Journal:  ACS Med Chem Lett       Date:  2022-06-01       Impact factor: 4.632

4.  Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands.

Authors:  Germán M Pérez; Luis A Salomón; Luis A Montero-Cabrera; José M García de la Vega; Marcello Mascini
Journal:  Mol Divers       Date:  2015-11-09       Impact factor: 2.943

5.  Unique identifiers for small molecules enable rigorous labeling of their atoms.

Authors:  Hesam Dashti; William M Westler; John L Markley; Hamid R Eghbalnia
Journal:  Sci Data       Date:  2017-05-23       Impact factor: 6.444

6.  Transformer-based artificial neural networks for the conversion between chemical notations.

Authors:  Lev Krasnov; Ivan Khokhlov; Maxim V Fedorov; Sergey Sosnin
Journal:  Sci Rep       Date:  2021-07-20       Impact factor: 4.379

  6 in total

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