where R is 8.314 J mol -1 K -1 and T is the temperature. We then retrained a production model on the full logP dataset without any validation or test splits using the same hyperparameters to predict logP of the molecules in the SAMPL6, SAMPL7, and SAMPL9 blind prediction challenges. The performance of Chemprop is shown in Table , where Chemprop outperforms all other submissions from the SAMPL6, SAMPL7 and SAMPL9 challenges. We note that submissions range from quantum mechanics (QM) models and molecular mechanics (MM) models to empirical models relying on heuristic rules or machine learning, as well as mixtures thereof. Our work therefore does not only outperform other empirical models, but also a large variety of QM and MM models. In general, logP is often used in drug development, where it serves as an indicator of lipophilicity, which is known to impact the absorption, distribution, metabolism, excretion, and toxicity of drug candidates. We thus demonstrate the ability of Chemprop to aid in important tasks such as drug discovery. Moreover, we note that the best performing submission in SAMPL7 was made by a biotechnology company independent of our group, using a Chemprop model trained on a different database, further highlighting the usefulness and impact of our software.