We also evaluated two common DL model architectures: multi-layer perceptron (MLP) neural networks (NN) and GNNs. MLPs are versatile and powerful models that have been shown to be capable of learning complex relationships between the input features and target variables. These models are made up of multiple interconnected layers of neurons with non-linear activation functions (e.g. ReLU, sigmoid, tanh). The weights and biases of the neurons are optimized over many iterations using gradient descent and backpropagation via the connections between neurons to minimize the loss function (mean squared error). However, they require careful architecture design, parameter tuning, and large quantities of training data. GNNs are neural network architectures designed to handle data that can be represented in the form of a graph. GNNs can effectively learn relationships and patterns in molecular structures because local features can be influenced by distant atoms and bonds through message-passing. Here we utilize the GraphNets architecture for GNN prediction, and the ChemProp message passing networks for generating the molecular embeddings. The tree-based models (RF, HGB, XGB, NGB) significantly outperform the DL models (i.e. MLP and GNN) because they are insensitive to irrelevant features and can learn irregular functions. Given the small size of our dataset, the limitations of DL models are pronounced. DL models can easily overfit to a small training set by learning from spurious correlations of irrelevant features and predict inaccurate output values.