Construction of a curated human pharmacokinetics database for molecular fragment analysis and machine learning applications

Drug Metab Dispos. 2026 Jun 2;54(7):100341. doi: 10.1016/j.dmd.2026.100341. Online ahead of print.

ABSTRACT

Pharmacokinetic (PK) data analysis in drug discovery is challenged by the inherent variability of experimental and clinical study designs, which hinders data integration and predictive modeling. To address this, we have curated a comprehensive, human-derived, and machine learning (ML)-ready PK dataset from authoritative, multiedition sources. This novel resource represents a systematically curated, human-derived PK dataset that integrates compound structures, clinical study design information, and experimental variability annotations, providing a structured foundation for data-driven analysis and modeling of human pharmacokinetics. We first implemented a rigorous standardization and filtering protocol to prepare the ML-ready dataset, and we demonstrated its utility through chemoinformatic analysis and ML classification model evaluations at across multiple classification systems. Fragment analysis showed a clear association between molecular structure and PK behavior, with hydrophilic fragments correlating with low distribution and high excretion, while lipophilic fragments were linked to enhanced absorption and plasma protein binding. By leveraging consensus predictions from an ensemble of classification models trained on calculated properties and molecular descriptors for each PK parameter, we achieved the most accurate predictions: ternary classifiers excelled in total clearance, whereas binary classifiers performed better for the others. In conclusion, this study provides a solid foundation for PK parameter classification and predictive modeling using a well curated human PK dataset. Integrating comprehensive data curation with ML presents a powerful strategy for accelerating drug design and enhancing rational therapeutic decision making. SIGNIFICANCE STATEMENT: Accurate prediction of absorption, distribution, metabolism, and excretion and pharmacokinetics (PK) properties is crucial for drug discovery and development. Data curation and availability is invaluable to the progress of the development of robust machine learning models to predict drug PK behavior. Standardized labeling and/or classification of human PK parameters would provide better interpretability and predictability. Integrating comprehensive data curation with machine learning presents a powerful strategy for accelerating drug design and enhancing rational therapeutic decision making.

PMID:42385657 | DOI:10.1016/j.dmd.2026.100341