Literature DB >> 10955522

Base information content in organic formulas

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Abstract

Three questions are addressed concerning organic formulas at their most primitive level: (1) What is the information per atomic symbol? (2) What is the level of system redundancy? (3) How are high-information formulas distinguished from low-information ones? The results are simple yet interesting. Carbon chemistry embodies a code which is low in base information and high in redundancy, irrespective of database size. Moreover, code units associated with halocarbons, proteins, and polynucleotides are especially high in information. Low-information units are more often associated with simple alkanes, aromatics, and common functional groups. Overall, the work for this paper quantifies the base information content in organic formulas; this contributes to research on symbolic language, chemical information, and molecular diversity.

Entities:  

Year:  2000        PMID: 10955522     DOI: 10.1021/ci990182k

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  4 in total

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Journal:  Int J Mol Sci       Date:  2014-09-24       Impact factor: 5.923

2.  Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems.

Authors:  Enrique Barreiro; Cristian R Munteanu; Maykel Cruz-Monteagudo; Alejandro Pazos; Humbert González-Díaz
Journal:  Sci Rep       Date:  2018-08-17       Impact factor: 4.379

3.  IFPTML Mapping of Drug Graphs with Protein and Chromosome Structural Networks vs. Pre-Clinical Assay Information for Discovery of Antimalarial Compounds.

Authors:  Viviana Quevedo-Tumailli; Bernabe Ortega-Tenezaca; Humberto González-Díaz
Journal:  Int J Mol Sci       Date:  2021-12-02       Impact factor: 5.923

4.  Alignment-Free Method to Predict Enzyme Classes and Subclasses.

Authors:  Riccardo Concu; M Natália D S Cordeiro
Journal:  Int J Mol Sci       Date:  2019-10-29       Impact factor: 5.923

  4 in total

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