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Gaussian process (GP) is a Bayesian model which provides several advantagesfor regression tasks in machine learning such as reliable quantitation ofuncertainty and improved interpretability. Their adoption has been precluded bytheir excessive computational cost and by the difficulty in adapting them foranalyzing sequences (e.g. amino acid and nucleotide sequences) and graphs (e.g.ones representing small molecules). In this study, we develop efficient andscalable approaches for fitting GP models as well as fast convolution kernelswhich scale linearly with graph or sequence size. We implement theseimprovements by building an open-source Python library called xGPR. We comparethe performance of xGPR with the reported performance of various deep learningmodels on 20 benchmarks, including small molecule, protein sequence and tabulardata. We show that xGRP achieves highly competitive performance with muchshorter training time. Furthermore, we also develop new kernels for sequenceand graph data and show that xGPR generally outperforms convolutional neuralnetworks on predicting key properties of proteins and small molecules.Importantly, xGPR provides uncertainty information not available from typicaldeep learning models. Additionally, xGPR provides a representation of the inputdata that can be used for clustering and data visualization. These resultsdemonstrate that xGPR provides a powerful and generic tool that can be broadlyuseful in protein engineering and drug discovery.

Linear-scaling kernels for protein sequences and small molecules  outperform deep learning while providing uncertainty quantitation and  improved interpretability