With the emerging ML methods, pKa predictors based on quantitative structure activity/property relationship (QSAR/QSPR) techniques have caught researchers' attention. The fundamental concept of QSPR models is to assume that structurally similar molecules tend to have similar properties. Thus, the mathematical relationship between pKa values and descriptors/features of a set of training compounds can be constructed by linear regression , support vector machine (SVM) , random forest (RF) , gradient boosting(GB) , extreme gradient boosting (XGB) , neural network (NN) and multiple other techniques. The descriptors often represent structural and electronic information of the compounds (e.g., molecular weight, number of hydrogen bond donor, polar surface area). Alternatively, molecular fingerprints such as MACCS , ECFP4 47 , Morgan , and Estate 49 fingerprints are used to encode molecule structures as binary strings which, in turn, encode for the presence and absence of specific substructures, fragments, and chemical features. As an example, the holistic pKa prediction studies based on the reliable and diverse iBond database () has reported that NN and XGB tend to perform better than other ML models (MAE = 0.87). A more recent study based on the S-pKa dataset which collected data from previous publications and other open access pKa database also proposed that the XGB model (MAE = 0.57 for acids, 0.55 for bases) outperformed SVM, RF, and NN models. Another study based on different data set have shown that RF (MAE: 0.68, RMSE: 1.03, R 2 : 82%) could be a better candidate compared with XGB.