  The absorption, distribution, metabolism, excretion, and toxicity (ADMET)
properties are important in drug discovery as they define efficacy and safety.
In this work, we applied an ensemble of features, including fingerprints and
descriptors, and a tree-based machine learning model, extreme gradient
boosting, for accurate ADMET prediction. Our model performs well in the
Therapeutics Data Commons ADMET benchmark group. For 22 tasks, our model is
ranked first in 18 tasks and top 3 in 21 tasks. The trained machine learning
models are integrated in ADMETboost, a web server that is publicly available at
https://ai-druglab.smu.edu/admet.
