Kernel function A kernel function, also known as the covariance function, describes the covariance of the GPR random variables. Together with the mean function, the kernel completes the definition of a GPR. The covariance matrix is generated by evaluating the kernel k. This kernel function receives two points x and x ′ ∈ R n as input (here amino acid descriptors, molecular fingerprints or embeddings from a pLM) and returns a similarity measure between these points in the form of a scalar. If point x and x ′ are considered to be similar by the kernel, then f (x) and f (x ′ ) are expected to be similar too. This function is evaluated for each pairwise combination of the points to retrieve the covariance matrix. The resulting covariance matrix K = k(X, X ′ ) is positive definite and symmetric. This also means that the covariance matrix is invertible. The choice of the kernel function is important since it imposes strong priors on the type of functions that can be modelled. In this study, we tested two different kernel functions: Radial Basis Function (RBF) and Tanimoto similarity (TS) kernels.