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

• Published in: Chemical engineering science 2018-06, Vol.183, p.95-105
• Main Authors: Zhou, Teng, Jhamb, Spardha, Liang, Xiaodong, Sundmacher, Kai, Gani, Rafiqul
• Format: Article
• Language: English
• Subjects: Acid dissociation constant; Amino acids; Artificial neural network; Group contribution method; Organic compounds; pKa
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2018.03.005

[Display omitted] •Prediction of acid dissociation constants (Ka) for a large set of organic compounds.•The Marrero and Gani–Group Contribution (MG-GC) method to develop the property models.•Linear and nonlinear GC models for amino acids and other classes of compounds.•An Artificial Neural Network (ANN) based GC model for organic compounds.•Modeling details and model parameters provided.•Accuracy of the models demonstrated through application examples. 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.