Journal article
Prediction of acid dissociation constants of organic compounds using group contribution methods
Technical University of Denmark1
Department of Chemical and Biochemical Engineering, Technical University of Denmark2
KT Consortium, Department of Chemical and Biochemical Engineering, Technical University of Denmark3
CERE – Center for Energy Ressources Engineering, Department of Chemical and Biochemical Engineering, Technical University of Denmark4
Max Planck Institute for Dynamics of Complex Technical Systems5
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.
| Language: | English |
|---|---|
| Publisher: | Elsevier |
| Year: | 2018 |
| Pages: | 95-105 |
| ISSN: | 00092509 and 18734405 |
| Types: | Journal article |
| DOI: | 10.1016/j.ces.2018.03.005 |
| ORCIDs: | 0000-0002-6719-9283 , Jhamb, Spardha and Liang, Xiaodong |
