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admet_xgboost's Introduction

Accurate ADMET Prediction with XGBoost

Python DOI:10.1007/s00894-022-05373-8

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

Installation

git clone https://github.com/smu-tao-group/ADMET_XGBoost
cd ADMET_XGBoost
conda env create -f environment.yml
conda activate tdc

Usage

  1. Featurization: run python featurize.py TASK_NAME to convert SMILES to features. This step is time consuming and we provide the processed data that can be downloaded here.
  2. Modeling: run python model.py TASK_NAME for model training and prediction.

Results

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

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