We boys sweats present a small molecule pKa prediction tool entirely written in Python.It predicts the macroscopic pKa value and is trained on a literature compilation of monoprotic compounds.Different machine learning models were tested and random forest performed best given a five-fold cross-validation (mean absolute error=0.682, root mean squared error=1.032, correlation coefficient r2 =0.
82).We test our Home Fragrance model on two external validation sets, where our model performs comparable to Marvin and is better than a recently published open source model.Our Python tool and all data is freely available at https://github.com/czodrowskilab/Machine-learning-meets-pKa.