In drug discovery, it is vital to confirm the predictions of pharmaceutical properties from computational models using costly wet-lab experiments. Hence, obtaining reliable uncertainty estimates is crucial for prioritizing drug molecules for subsequent experimental validation. Conformal Prediction (CP) is a promising tool for creating such prediction sets for molecular properties with a coverage guarantee. However, the exchangeability assumption of CP is often challenged with covariate shift in drug discovery tasks: Most datasets contain limited labeled data, which may not be representative of the vast chemical space from which molecules are drawn. To address this limitation, we propose a method called CoDrug that employs an energy-based model leveraging both training data and unlabelled data, and Kernel Density Estimation (KDE) to assess the densities of a molecule set. The estimated densities are then used to weigh the molecule samples while building prediction sets and rectifying for distribution shift. In extensive experiments involving realistic distribution drifts in various small-molecule drug discovery tasks, we demonstrate the ability of CoDrug to provide valid prediction sets and its utility in addressing the distribution shift arising from de novo drug design models. On average, using CoDrug can reduce the coverage gap by over 35% when compared to conformal prediction sets not adjusted for covariate shift.

翻译：在药物发现中，利用昂贵的湿实验验证计算模型对药理学性质的预测至关重要。因此，获得可靠的置信估计对于优先筛选药物分子进行后续实验验证十分关键。共形预测（CP）是一种能够为分子性质构建带覆盖保证的预测集的有效工具，但CP的可交换性假设在药物发现任务中常受到协变量偏移的挑战：大部分数据集仅包含有限的标注数据，这些数据可能无法代表分子所来源的广阔化学空间。为解决此局限，我们提出CoDrug方法：该方法采用基于能量的模型，同时利用训练数据和未标注数据，并结合核密度估计（KDE）评估分子集的密度。随后利用估计密度对构建预测集过程中的分子样本进行加权，并对分布偏移进行校正。在涉及多种小分子药物发现任务中实际分布偏移的大规模实验中，我们展示了CoDrug能够提供有效的预测集，并证明了其在解决全新药物设计模型产生的分布偏移问题中的实用性。与未针对协变量偏移调整的共形预测集相比，采用CoDrug平均可降低超过35%的覆盖缺口。