Building on the procedure outlined by Ree et al., we employ the Optuna framework (v. 3.3.0) to identify optimal hyper-parameters for LigthGBM regression and classification models. 6 Specifically, the Bayesian optimization technique utilizing the tree-structured Parzen estimator (TPE) is applied for hyperparameter space exploration. For the regression task, the target value is the QM computed pK a values. For the binary classification task, which aims to predict the site with the lowest QM computed pK a value, labels are assigned in the following manner: '1' for the lowest QM computed pK a value (true site) and '0' for all other QM computed pK a values. As there sometimes is a slight variation between the pK a value and the other pK a values, we also introduce a tolerance where a pK a value within +1 pK a units or +2 pK a units of the lowest pK a value accepted as '1' to account for these variations, see SI -Machine learning models section for more information. Further, given the significant imbalance between the two classes (with '0's far outnumbering '1's), the hyperparameter scale pos weight is invoked during hyperparameter optimization. Finally, we establish a "Null model" for the classification task, wherein all sites are predicted as '0'.