For the SAMPL challenges in general, methods submitted as part of the blind portion are classified into the following categories: empirical methods (data-driven methods, e.g., machine learning), physical quantum mechanics (QM)-based, physical molecular mechanics (MM)-based, and Mixed (combination of the other categories). The SAMPL9 logP prediction challenge focused on the toluene-water partition coefficients (logPtol/w) and most submissions followed nonequilibrium alchemical approaches and/or empirical/ machine learning approaches. Machine learning has become an increasing presence in the chemical space through drug screening efforts, conformational searches, and is used primarily in cheminformatics. Pertaining to the SAMPL competitions, neural network approaches have been generated as a tool for understanding the quantitative structure-property relationships, or QSPR. For SAMPL6, a deep learning approach with five hidden layers using extended connectivity fingerprints was constructed to predict the octanol-water partition coefficient (logPo/w). The neural network was trained on 14176 data points to predict the logPo/w for 11 kinase-fragment molecules, which were well-represented in their training data, with a MAE of 0.51 logPo/w units. As part of SAMPL7, Donyapour and Dickson used a multilayer perceptron (MLP) neural network to train their Graphical Scattering for Graphs models. This neural network approach achieved a mean absolute error (MAE) of 0.44 logPo/w units for a series of drug-like molecules with a sulfonyl moiety. These methods showcase the successes of using larger datasets available for logPo/w and how those can be effectively trained to predict logPo/w for a series of drug-like molecules with similar scaffolds and moieties.