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

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

Cite this

@article{bb6177fb10e34ea99c51c24c218a9fbf,
title = "Prediction of acid dissociation constants of organic compounds using group contribution methods",
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.",
keywords = "Acid dissociation constant, pKa, Group contribution method, Artificial neural network, Amino acids, Organic compounds",
author = "Teng Zhou and Spardha Jhamb and Xiaodong Liang and Kai Sundmacher and Rafiqul Gani",
year = "2018",
doi = "10.1016/j.ces.2018.03.005",
language = "English",
volume = "183",
pages = "95--105",
journal = "Chemical Engineering Science",
issn = "0009-2509",
publisher = "Pergamon Press",

}

Prediction of acid dissociation constants of organic compounds using group contribution methods. / Zhou, Teng; Jhamb, Spardha; Liang, Xiaodong; Sundmacher, Kai; Gani, Rafiqul.

In: Chemical Engineering Science, Vol. 183, 2018, p. 95-105.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Prediction of acid dissociation constants of organic compounds using group contribution methods

AU - Zhou, Teng

AU - Jhamb, Spardha

AU - Liang, Xiaodong

AU - Sundmacher, Kai

AU - Gani, Rafiqul

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - Acid dissociation constant

KW - pKa

KW - Group contribution method

KW - Artificial neural network

KW - Amino acids

KW - Organic compounds

U2 - 10.1016/j.ces.2018.03.005

DO - 10.1016/j.ces.2018.03.005

M3 - Journal article

VL - 183

SP - 95

EP - 105

JO - Chemical Engineering Science

JF - Chemical Engineering Science

SN - 0009-2509

ER -