Drug discovery under covariate shift with domain-informed prior distributions over functions

Conference item

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—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.

Authors: Klarner, L; Rudner, T; Reutlinger, M; Schindler, T; Morris, G

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Journal of Machine Learning Research
• Volume: 202
• Pages: 17176-17197
• Series: Proceedings of Machine Learning Research
• Publication date: 2023-08-31
• Acceptance date: 2023-06-14
• Event title: 40th International Conference on Machine Learning (ICML 2023)
• Event location: Honolulu, Hawaii, USA
• Event website: https://icml.cc/Conferences/2023/Dates
• Event start date: 2023-07-23
• Event end date: 2023-07-29
• ISSN: 2640-3498
• Language: English
• Keywords: FFR