Background

The goal of assessing ADME properties is to understand how a potential drug candidate interacts with the human body, including absorption, distribution, metabolism, and excretion. This knowledge is crucial for evaluating efficacy, safety, and clinical potential, guiding drug development for optimal therapeutic outcomes. Fang et al. (2023) disclosed DMPK datasets collected over 20 months across six ADME in vitro endpoints: human and rat liver microsomal stability, MDR1-MDCK efflux ratio, solubility, and human and rat plasma protein binding. The dataset includes between 885 and 3,087 measurements for each corresponding endpoint. The compounds show chemical diversity across all ranges of the endpoints which are microsomal stability, plasma protein binding, permeability, and solubility.

Benchmarking

The goal* of this benchmark is to perform a single task, which is to have the best predictive model for rat liver microsomal stability.

Description of readout

• Readouts: LOG RLM_CLint (mL/min/kg)
• Bioassay readout: Intrinsic clearance
• Optimization objective: Higher value

Molecule data resource

Reference: https://doi.org/10.1021/acs.jcim.3c00160

Train/test split

In this benchmark set, the same train/test sets as in the fang2023 paper were used for the 6 endpoints: human and rat liver microsomal stability, MDR1-MDCK efflux ratio, solubility, and human and rat plasma protein binding, respectively. See more details at https://github.com/molecularinformatics/Computational-ADME/tree/main/MPNN.

Distribution of the train/test in the chemical space

Related links

The full curation and creation process is documented here.