Drug discovery and development is a complex and costly process, with a substantial portion of the expense dedicated to characterizing the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of new drug candidates. While the advent of deep learning and molecular graph neural networks (GNNs) has significantly enhanced in silico ADMET prediction capabilities, reliably quantifying prediction uncertainty remains a critical challenge. The performance of GNNs is influenced by both the volume and the quality of the data. Hence, determining the reliability and extent of a prediction is as crucial as achieving accurate predictions, especially for out-of-domain (OoD) compounds. This paper introduces a novel GNN model called conformalized fusion regression (CFR). CFR combined a GNN model with a joint mean-quantile regression loss and an ensemble-based conformal prediction (CP) method. Through rigorous evaluation across various ADMET tasks, we demonstrate that our framework provides accurate predictions, reliable probability calibration, and high-quality prediction intervals, outperforming existing uncertainty quantification methods.

中文翻译：

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用于分子 ADMET 特性预测和可靠不确定性量化的保形图学习。


药物发现和开发是一个复杂且昂贵的过程，其中很大一部分费用专门用于表征新候选药物的吸收、分布、代谢、排泄和毒性 （ADMET） 特性。虽然深度学习和分子图神经网络 （GNN） 的出现显著增强了计算机模拟 ADMET 预测能力，但可靠地量化预测不确定性仍然是一项关键挑战。GNN 的性能受数据量和质量的影响。因此，确定预测的可靠性和范围与实现准确预测一样重要，尤其是对于域外 （OoD） 化合物。本文介绍了一种称为共形融合回归 （CFR） 的新型 GNN 模型。CFR 将 GNN 模型与联合均值分位数回归损失和基于集成的共形预测 （CP） 方法相结合。通过对各种 ADMET 任务的严格评估，我们证明我们的框架提供了准确的预测、可靠的概率校准和高质量的预测区间，优于现有的不确定性量化方法。