Enhancing molecular property prediction through data integration and consistency assessment.

ABSTRACT: Data heterogeneity and distributional misalignments pose critical challenges for machine learning models, often compromising predictive accuracy. These challenges are exemplified in preclinical safety modeling, a crucial step in early-stage drug discovery where limited data and experimental constraints exacerbate integration issues. Analyzing public ADME datasets, we uncovered significant misalignments as well as inconsistent property annotations between gold-standard and popular benchmark sources, such as Therapeutic Data Commons. These dataset discrepancies, which can arise from differences in various factors, including experimental conditions in data collection as well as chemical space coverage, can introduce noise and ultimately degrade model performance. Data standardization, despite

SUBMITTER: Parrondo-Pizarro R 

PROVIDER: S-EPMC12574261 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature