Benchmarking goal:

Single tasks for the six endpoints: As the original paper, author established regression tasks for each ADME endpoints with predefined train-test set for the model training. In this benchmark set, the same train/test sets 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.

Benchmarking

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

Description of readout

• Readouts: LOG PLASMA PROTEIN BINDING (RAT) (% unbound)
• Bioassay readout: Rat plasma protein binding
• Optimization objective: Lower 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 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.