Literature DB >> 10850791

Modeling of ion complexation and extraction using substructural molecular fragments

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Abstract

A substructural molecular fragment (SMF) method has been developed to model the relationships between the structure of organic molecules and their thermodynamic parameters of complexation or extraction. The method is based on the splitting of a molecule into fragments, and on calculations of their contributions to a given property. It uses two types of fragments: atom/bond sequences and "augmented atoms" (atoms with their nearest neighbors). The SMF approach is tested on physical properties of C2-C9 alkanes (boiling point, molar volume, molar refraction, heat of vaporization, surface tension, melting point, critical temperature, and critical pressures) and on octanol/water partition coefficients. Then, it is applied to the assessment of (i) complexation stability constants of alkali cations with crown ethers and phosphoryl-containing podands, and of beta-cyclodextrins with mono- and 1,4-disubstituted benzenes, and (ii) solvent extraction constants for the complexes of uranyl cation by phosphoryl-containing ligands.

Entities:  

Year:  2000        PMID: 10850791     DOI: 10.1021/ci9901340

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


  6 in total

1.  Substructural fragments: an universal language to encode reactions, molecular and supramolecular structures.

Authors:  A Varnek; D Fourches; F Hoonakker; V P Solov'ev
Journal:  J Comput Aided Mol Des       Date:  2005-11-16       Impact factor: 3.686

2.  Surrogate data--a secure way to share corporate data.

Authors:  Igor V Tetko; Ruben Abagyan; Tudor I Oprea
Journal:  J Comput Aided Mol Des       Date:  2005-11-03       Impact factor: 3.686

3.  Predictive cartography of metal binders using generative topographic mapping.

Authors:  Igor I Baskin; Vitaly P Solov'ev; Alexander A Bagatur'yants; Alexandre Varnek
Journal:  J Comput Aided Mol Des       Date:  2017-07-07       Impact factor: 3.686

4.  Applied machine learning for predicting the lanthanide-ligand binding affinities.

Authors:  Suryanaman Chaube; Sriram Goverapet Srinivasan; Beena Rai
Journal:  Sci Rep       Date:  2020-08-31       Impact factor: 4.379

5.  Tuning HERG out: antitarget QSAR models for drug development.

Authors:  Rodolpho C Braga; Vinicius M Alves; Meryck F B Silva; Eugene Muratov; Denis Fourches; Alexander Tropsha; Carolina H Andrade
Journal:  Curr Top Med Chem       Date:  2014       Impact factor: 3.295

6.  QSPR ensemble modelling of the 1:1 and 1:2 complexation of Co²⁺, Ni²⁺, and Cu²⁺ with organic ligands: relationships between stability constants.

Authors:  Vitaly Solov'ev; Alexandre Varnek; Aslan Tsivadze
Journal:  J Comput Aided Mol Des       Date:  2014-04-16       Impact factor: 3.686

  6 in total

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