  Neural processes (NPs) are models for transfer learning with properties
reminiscent of Gaussian Processes (GPs). They are adept at modelling data
consisting of few observations of many related functions on the same input
space and are trained by minimizing a variational objective, which is
computationally much less expensive than the Bayesian updating required by GPs.
So far, most studies of NPs have focused on low-dimensional datasets which are
not representative of realistic transfer learning tasks. Drug discovery is one
application area that is characterized by datasets consisting of many chemical
properties or functions which are sparsely observed, yet depend on shared
features or representations of the molecular inputs. This paper applies the
conditional neural process (CNP) to DOCKSTRING, a dataset of docking scores for
benchmarking ML models. CNPs show competitive performance in few-shot learning
tasks relative to supervised learning baselines common in chemoinformatics, as
well as an alternative model for transfer learning based on pre-training and
refining neural network regressors. We present a Bayesian optimization
experiment which showcases the probabilistic nature of CNPs and discuss
shortcomings of the model in uncertainty quantification.
