Experimental logP data of the SAMPL6, SAMPL7 and SAMPL9 challenges was downloaded from the SAMPL GitHub repository. SAMPL runs a series of blind challenges for compu- tational chemistry, providing the identity of test molecules for which predictions of physicochemical properties, among them the water-octanol partition coefficients, can be submitted using quantum-mechanics, molecular mechanics, or empirical models. In this work, we build an empirical model based on Chemprop, which we train on a publicly available dataset of logP measurements from Ref. 16. Molecules present in the SAMPL challenges were removed from the logP training dataset. The remaining 23,469 data points were randomly split into 80% training, 10% validation, and 10% test data. The test data was used to obtain a measure of performance for the literature dataset. We then retrained a production model on the full dataset (no validation or test data) using the best hyperparameters and number of epochs identified earlier, with which we made predictions for the three SAMPL challenges.