Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

• URL: http://arxiv.org/abs/2307.15073v1
• Date: Fri, 14 Jul 2023 05:01:10 GMT
• Title: Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
• Authors: Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M. Morris, Charlotte Deane, Yee Whye Teh
• Abstract summary: Real-world drug discovery tasks are often characterized by a scarcity of labeled data and a significant range of data. We present a principled way to encode explicit prior knowledge of the data-generating process into a prior distribution. We demonstrate that using integrate Q-SAVI to contextualize prior knowledgelike chemical space into the modeling process affords substantial accuracy and calibration.
• Score: 30.305418761024143
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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