Obtaining accurate and valid information for drug molecules is a crucial and
challenging task. However, chemical knowledge and information have been
accumulated over the past 100 years from various regions, laboratories, and
experimental purposes. Little has been explored in terms of the
out-of-distribution (OOD) problem with noise and inconsistency, which may lead
to weak robustness and unsatisfied performance. This study proposes a novel
benchmark ADMEOOD, a systematic OOD dataset curator and benchmark specifically
designed for drug property prediction. ADMEOOD obtained 27 ADME (Absorption,
Distribution, Metabolism, Excretion) drug properties from Chembl and relevant
literature. Additionally, it includes two kinds of OOD data shifts: Noise Shift
and Concept Conflict Drift (CCD). Noise Shift responds to the noise level by
categorizing the environment into different confidence levels. On the other
hand, CCD describes the data which has inconsistent label among the original
data. Finally, it tested on a variety of domain generalization models, and the
experimental results demonstrate the effectiveness of the proposed partition
method in ADMEOOD: ADMEOOD demonstrates a significant difference performance
between in-distribution and out-of-distribution data. Moreover, ERM (Empirical
Risk Minimization) and other models exhibit distinct trends in performance
across different domains and measurement types.

该研究提出了一种针对药物属性预测的系统的 OOD 数据集整理器和基准 ADMEOOD，其中包括噪声转移和概念冲突漂移两种 OOD 数据转移方式，并经过多个领域泛化模型测试，实验结果表明 ADMEOOD 在分布内和分布外数据上有明显不同的性能表现，ERM 和其他模型在不同领域和测量类型上展现出不同的趋势。