论文标题
用XGBoost准确预测
Accurate ADMET Prediction with XGBoost
论文作者
论文摘要
吸收，分布，代谢，排泄和毒性（ADMET）特性在药物发现中很重要，因为它们定义了功效和安全性。在这项工作中，我们应用了一系列功能，包括指纹和描述符，以及基于树的机器学习模型，极端的梯度提升，以进行准确的ADMET预测。我们的模型在Therapeutics Data Commons ADMET基准组中表现良好。对于22个任务，我们的模型在21个任务中的18个任务中排名第一，排名前3位。训练有素的机器学习模型集成在AdmetBoost，这是一家网络服务器，该网络服务器可在https://ai-druglab.smu.edu/admet上公开获得。
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.
