Technology Category
Machine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Uncertainty RepresentationsKnowledge Representation and Reasoning: Qualitative Reasoning
Temporal Distribution Shift in Real-World Pharmaceutical Data: Implications for Uncertainty Quantification in QSAR Models
🤖 AI Summary
Temporal distribution shift in real-world drug discovery undermines the reliability of uncertainty quantification (UQ) for QSAR models. Method: We systematically evaluate mainstream UQ methods—including Deep Ensembles and Monte Carlo Dropout—under non-i.i.d. temporal data, leveraging large-scale, real pharmaceutical time-series datasets. We introduce a novel drift quantification framework based on Wasserstein distance and PCA trajectory analysis to characterize coupled shifts in label space and molecular descriptor space. Contribution/Results: We establish the first quantitative linkage among drift intensity, assay type, and UQ failure modes. Our analysis reveals that significant temporal drift is pervasive; increasing drift severity degrades UQ calibration substantially, with some methods exhibiting >40% long-term coverage degradation. This work provides the first UQ evaluation benchmark explicitly designed for temporal distribution shift in AI-driven drug discovery, along with empirically grounded failure预警 criteria.
Technology Category
Application Category
📝 Abstract
The estimation of uncertainties associated with predictions from quantitative structure-activity relationship (QSAR) models can accelerate the drug discovery process by identifying promising experiments and allowing an efficient allocation of resources. Several computational tools exist that estimate the predictive uncertainty in machine learning models. However, deviations from the i.i.d. setting have been shown to impair the performance of these uncertainty quantification methods. We use a real-world pharmaceutical dataset to address the pressing need for a comprehensive, large-scale evaluation of uncertainty estimation methods in the context of realistic distribution shifts over time. We investigate the performance of several uncertainty estimation methods, including ensemble-based and Bayesian approaches. Furthermore, we use this real-world setting to systematically assess the distribution shifts in label and descriptor space and their impact on the capability of the uncertainty estimation methods. Our study reveals significant shifts over time in both label and descriptor space and a clear connection between the magnitude of the shift and the nature of the assay. Moreover, we show that pronounced distribution shifts impair the performance of popular uncertainty estimation methods used in QSAR models. This work highlights the challenges of identifying uncertainty quantification methods that remain reliable under distribution shifts introduced by real-world data.
Problem
Research questions and friction points this paper is trying to address.
Evaluate uncertainty estimation methods under temporal distribution shifts
Assess impact of distribution shifts on QSAR model predictions
Identify reliable uncertainty quantification in real-world pharmaceutical data
Innovation
Methods, ideas, or system contributions that make the work stand out.
real-world pharmaceutical dataset
ensemble and Bayesian approaches
systematic assessment of distribution shifts
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