Background

The goal of assessing ADME properties is to understand how a potential drug candidate interacts with the human body, encompassing absorption, distribution, metabolism, and excretion. This knowledge is crucial for evaluating the drug's efficacy, safety, and clinical potential, ultimately guiding drug development towards optimal therapeutic outcomes. Fang et al. (2023) disclosed DMPK datasets collected over 20 months, covering 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.

Benchmarking

The goal of this benchmark is to perform a single task, which is to have the best predictive model for solubility.

Description of readout

• Readouts: LOG_SOLUBILITY
• Bioassay readout: Solubility
• 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.