In this paper, group contribution (GC) property models for the estimation of acid dissociation constants (K-a) of organic compounds are presented. Three GC models are developed to predict the negative logarithm of the acid dissociation constant pK(a): (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 pK(a) values are also provided. The model details, regressed parameters and application examples are highlighted. (C) 2018 Elsevier Ltd. All rights reserved.