  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.
