Conformal Prediction for Molecular Properties under Label Shift

2026-08-18

AI Summary

该研究针对药物分子性质预测中的标签偏移问题，提出了一种基于保形预测框架的方法，通过加权保形分数来提供统计严格的预测区间，从而提高AI预测的可靠性和不确定性量化。

Abstract

Drug discovery and development underpins healthcare but remains costly and failure-prone. A critical bottleneck lies in predicting molecular properties such as solubility, potency, and toxicity, which directly determine whether a candidate can advance from preclinical to clinical trials. Artificial Intelligence (AI) has accelerated this process, yet its reliability is often undermined by distribution shift, as experimental conditions frequently diverge from training data. In addition, conventional point predictions provide only single-value estimates, offering limited guidance for high-stakes experimental design. We address these challenges with a conformal prediction framework tailored to label shift. By weighting conformal scores using marginal label probability ratios, our method produces statistically rigorous prediction intervals without retraining. This enables robust uncertainty quantification even when property distributions drift, directly tackling one of the most pervasive obstacles to applying AI in real-world drug development. By moving beyond accuracy alone to provide actionable confidence measures, our approach enhances the trustworthiness of AI-driven predictions. This further aligns predictive modeling with regulatory demands for transparency and uncertainty reporting and ultimately supports more reliable decision-making in billion-dollar development pipelines.

Problem

• Conformal Prediction
• Label Shift
• Molecular Properties
• Uncertainty Quantification
• Drug Discovery

Innovation

• Conformal Prediction
• Label Shift
• Uncertainty Quantification
• Molecular Properties