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Accurate ADMET Prediction with XGBoost
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Accurate ADMET Prediction with XGBoost
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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.
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Cited by 1 Pith paper
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Quantum-Enhanced Multi-Task Learning with Learnable Weighting for Pharmacokinetic and Toxicity Prediction
Quantum descriptors plus a learnable data-scale loss weight let one multi-task model beat single-task Chemprop-RDKit on 12 of 13 ADMET classification tasks.
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