Accurate ADMET Prediction with XGBoost
This work addresses the need for accurate ADMET prediction to improve drug efficacy and safety, but it is incremental as it uses an existing method on a specific benchmark.
The paper tackled the problem of predicting ADMET properties in drug discovery by applying XGBoost with ensemble features, achieving top rankings in 18 out of 22 benchmark tasks.
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.