# Accurate ADMET Prediction with XGBoost

arXiv: https://arxiv.org/abs/2204.07532 · HTML: https://mlsanity.com/paper/2204.07532/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 15 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development](https://mlsanity.com/paper/2102.09548/) (2021)
- [Multiscale Detection of Cancerous Tissue in High Resolution Slide Scans](https://mlsanity.com/paper/2010.00641/) (2020)

## Built on by (most cited first, 2 of 2)

- [MolE: a molecular foundation model for drug discovery](https://mlsanity.com/paper/2211.02657/) (2022)
- [Current Methods for Drug Property Prediction in the Real World](https://mlsanity.com/paper/2309.17161/) (2023)

## Abstract (arXiv)

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.