Drug Discovery under Covariate Shift with Domain-Informed Prior
  Distributions over Functions
Leo Klarner Tim G. J. Rudner Michael Reutlinger Torsten Schindler Garrett M. Morris Charlotte Deane Yee Whye Teh
Accelerating the discovery of novel and more effective therapeutics is an
important pharmaceutical problem in which deep learning is playing an
increasingly significant role. However, real-world drug discovery tasks are
often characterized by a scarcity of labeled data and significant covariate
shift$\unicode{x2013}\unicode{x2013}$a setting that poses a challenge to
standard deep learning methods. In this paper, we present Q-SAVI, a
probabilistic model able to address these challenges by encoding explicit prior
knowledge of the data-generating process into a prior distribution over
functions, presenting researchers with a transparent and probabilistically
principled way to encode data-driven modeling preferences. Building on a novel,
gold-standard bioactivity dataset that facilitates a meaningful comparison of
models in an extrapolative regime, we explore different approaches to induce
data shift and construct a challenging evaluation setup. We then demonstrate
that using Q-SAVI to integrate contextualized prior knowledge of drug-like
chemical space into the modeling process affords substantial gains in
predictive accuracy and calibration, outperforming a broad range of
state-of-the-art self-supervised pre-training and domain adaptation techniques.