Abstract
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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Acknowledgements
Computational time was generously provided by Southern Methodist University’s Center for Research Computing. The preprint version of this work is available on arXiv with DOI number 2204.07532 under CC BY-NC-ND 4.0 license.
Funding
Research reported in this paper was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R15GM122013.
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HT and RK conducted the experiment. HT plotted figures. All authors revised the manuscript.
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The data used in this study is publicly available in TDC ADMET benchmark group https://tdcommons.ai/benchmark/admet_group/overview/. The dataset can be downloaded through the TDC Python package (v0.3.6). The default training and testing data were used for model training. We shared the related codes, model parameters for each task, and the ready-to-use featurization results on GitHub at https://github.com/smu-tao-group/ADMET_XGBoost. The web server can be accessed at https://ai-druglab.smu.edu/admet.
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Hao Tian and Rajas Ketkar contributed equally to this work.
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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
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DOI: https://doi.org/10.1007/s00894-022-05373-8