If you find it useful, please cite: Tian, H., Ketkar, R. & Tao, P. ADMETboost: a web server for accurate ADMET prediction. J Mol Model 28, 408 (2022). https://doi.org/10.1007/s00894-022-05373-8
git clone https://github.com/smu-tao-group/ADMET_XGBoost
cd ADMET_XGBoost
conda env create -f environment.yml
conda activate tdc- Featurization: run
python featurize.py TASK_NAMEto convert SMILES to features. This step is time consuming and we provide the processed data that can be downloaded here. - Modeling: run
python model.py TASK_NAMEfor model training and prediction.
| Tasks | Evaluation | Performance | Rank |
|---|---|---|---|
| Absorption | |||
| Caco2 | MAE | 0.288 ± 0.011 | 1st |
| HIA | AUROC | 0.987 ± 0.002 | 1st |
| Pgp | AUROC | 0.911 ± 0.002 | 5th |
| Bioav | AUROC | 0.700 ± 0.010 | 2nd |
| Lipo | MAE | 0.533 ± 0.005 | 1st |
| AqSol | MAE | 0.727 ± 0.004 | 1st |
| Distribution | |||
| BBB | AUROC | 0.905 ± 0.001 | 1st |
| PPBR | MAE | 8.251 ± 0.115 | 1st |
| VDss | Spearman | 0.612 ± 0.018 | 1st |
| Metabolism | |||
| CYP2C9 Inhibition | AUPRC | 0.794 ± 0.004 | 3rd |
| CYP2D6 Inhibition | AUPRC | 0.721 ± 0.003 | 2nd |
| CYP3A4 Inhibition | AUPRC | 0.877 ± 0.002 | 3rd |
| CYP2C9 Substrate | AUPRC | 0.387 ± 0.018 | 3rd |
| CYP2D6 Substrate | AUPRC | 0.648 ± 0.023 | 5th |
| CYP3A4 Substrate | AUPRC | 0.680 ± 0.005 | 1st |
| Excretion | |||
| Half Life | Spearman | 0.396 ± 0.027 | 1st |
| CL-Hepa | Spearman | 0.420 ± 0.011 | 2nd |
| CL-Micro | Spearman | 0.587 ± 0.006 | 2nd |
| Toxicity | |||
| LD50 | MAE | 0.602 ± 0.006 | 2nd |
| hERG | AUROC | 0.806 ± 0.005 | 4th |
| Ames | AUROC | 0.859 ± 0.002 | 1st |
| DILI | AUROC | 0.933 ± 0.011 | 1st |