Kernel methods apply a non-linear transformation (e.g. radial basis function, polynomial, sigmoid, etc) to the input features, known as the kernel trick, which can then be used in many different algorithms. Support vector regression (SVR) uses this approach to find an optimal hyperplane in the same or higher dimension that maximizes the margin (distance) between the hyperplane and the nearest data points while also minimizing the prediction error. SVR is effective for handling high-dimensional or non-linear data and outliers. However, it does not scale efficiently for very large datasets. Kernel ridge regression (KRR) is a regularization technique that takes advantage of the kernel trick in order to learn a linear relationship in the kernel space, and a non-linear relationship in the original space similar to SVR. Unlike SVR, KRR uses a squared error loss while SVR calculates the loss from the distance between the test set and the decision boundary of the hyperplane which is determined by the user (i.e. epsilon). Gaussian process regression (GPR) also uses the kernel trick, but it takes a Bayesian approach. GPs are probabilistic models that learn the probability distribution (posterior) over all data points. A prior distribution, typically a Gaussian distribution, over all possible functions to model the data must be specified, with the covariance of the distributions determined by the kernel function. The updated distribution or posterior distribution incorporates information from the prior distribution and the dataset using Bayes' theorem which is then used to make predictions on new, unseen data points. It is particularly useful for small datasets and can capture complex relationships with uncertainty estimates. The biggest disadvantage of GPs is the high computational demand which remains a challenge for high-dimensional and large datasets. In general, KRR and GPR are out-performed by other methods on all representations except for ECFP, where the Tanimoto kernel 56 can be employed, leading to good predictive performance (R 2 scores of 0.52±0.02 and 0.56±0.02, respectively). On the other hand, SVR is comparable to the top methods for most representations, including R 2 scores of 0.53±0.03 with ECFP and 0.56±0.03 with Mordred descriptors.