where ∆G F EP (A) is the hydration free energy of molecule A calculated by the alchemical method, and ∆G EXP (A) is the experimentally determined hydration free energy for the same molecule. For a given training set with defined descriptors, machine-learning models were used to fit the training domain using five-fold cross-validation over 10 replicates, resulting in a total population N pop of 50 trained models (see methods section below). All individual models in N pop are regression models predicting their own ∆ Ĝoffset value. We define our offset estimator as the arithmetic mean of these offset values, and use the standard deviation of the mean as a measure of the precision of the calculated offset. Thus we define a corrected hydration free energy as: