Prediction of acid dissociation constants of organic compounds using group contribution methods

Teng Zhou, Spardha Jhamb, Xiaodong Liang, Kai Sundmacher, Rafiqul Gani*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

In this paper, group contribution (GC) property models for the estimation of acid dissociation constants (Ka) of organic compounds are presented. Three GC models are developed to predict the negative logarithm of the acid dissociation constant pKa: (a) a linear GC model for amino acids using 180 data-points with average absolute error of 0.23; (b) a non-linear GC model for organic compounds using 1622 data-points with average absolute error of 1.18; (c) an artificial neural network (ANN) based GC model for the organic compounds with average absolute error of 0.17. For each of the developed model, uncertainty estimates for the predicted pKa values are also provided. The model details, regressed parameters and application examples are highlighted.
Original languageEnglish
JournalChemical Engineering Science
Volume183
Pages (from-to)95-105
ISSN0009-2509
DOIs
Publication statusPublished - 2018

Keywords

  • Acid dissociation constant
  • pKa
  • Group contribution method
  • Artificial neural network
  • Amino acids
  • Organic compounds

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