Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
This addresses the challenge of accelerating therapeutic discovery for pharmaceutical research, though it is incremental as it builds on existing probabilistic modeling approaches.
The paper tackles the problem of drug discovery under covariate shift and data scarcity by introducing Q-SAVI, a probabilistic model that incorporates domain knowledge into prior distributions over functions, resulting in substantial gains in predictive accuracy and calibration over state-of-the-art methods.
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.