Topological and constitutional descriptors were calculated with the software 'rcdk' package in R 115 while experimental measurements (i.e., logPN, pKa, and pH) were added from our dataset. We also added the free energies of hydration and hydrogen bond strengths computed using the new open-source tool 'Jazzy'. The H-bond donor and acceptor strengths were obtained by calculating the partial charges of the hydrogen atoms and atoms with lone electron pairs, respectively, along with corrective terms. The free energy of hydration was calculated using the sum of the polar, apolar, and interaction terms. The polar term was derived from the previously calculated H-bond donor and acceptor strengths. The apolar terms consist of the sum of the weighted contributions of the topological surface area, number of rings, and p-orbital counts in the sp and sp 2 atoms. The interaction term consists of a weighted sum of the amount of neighboring H-bond acceptor groups each atom has in a molecule. We eliminated intercorrelated properties so that no descriptor had a correlation value of r 2 > 0.6 (Figure and). After this filtration step, two different feature selection methods were tested to choose the best descriptors for the Machine Learning models. First, we performed Welch's t-test (WTT), which evaluates the statistical difference between the means of two populations that have unequal variances and sample sizes. The algorithm calculates the mean of both groups from the binomial conditional for each descriptor. These values were evaluated using Equation .