| Autor: |
Klarner, L, Rudner, T, Reutlinger, M, Schindler, T, Morris, G, Deane, CM, Yeh, YW |
| Přispěvatelé: |
Krause, A, Brunskill, E, Cho, K, Engelhardt, B, Sabato, S, Scarlett, J |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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