标签偏移下分子性质的共形预测
Conformal Prediction for Molecular Properties under Label Shift
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中文总结 AI 辅助
针对药物开发中分子性质预测的分布偏移与传统点预测指导有限的问题，提出标签偏移下的共形预测框架，无需重训练即可生成严谨预测区间，提升AI预测可信度与决策可靠性。
中文摘要 AI 辅助
药物发现与开发是医疗保健的基础，但仍成本高昂且易失败。一个关键瓶颈在于预测溶解度、效力、毒性等分子性质，这些性质直接决定候选药物能否从临床前研究推进至临床试验。人工智能（AI）已加速这一进程，但其可靠性常因分布偏移而受损，因为实验条件往往与训练数据存在差异。此外，传统点预测仅提供单值估计，对高风险实验设计的指导有限。我们针对标签偏移问题设计了共形预测框架，通过使用边际标签概率比率对共形得分进行加权，该方法无需重新训练即可生成统计上严谨的预测区间。即使在性质分布发生漂移时，这也能实现鲁棒的不确定性量化，直接解决AI在实际药物开发中应用的最普遍障碍之一。我们的方法超越了单纯的准确性，提供可操作的置信度度量，提升了AI驱动预测的可信度，进一步使预测建模符合监管机构对透明度和不确定性报告的要求，最终支持数十亿美元开发管线中更可靠的决策制定。
英文摘要
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.
发表机构
- Mogam Institute for Biomedical Research（慕庵生物医学研究院）
- Intellicode（智码公司）
机构由 AI 辅助整理，请以论文原文为准。
