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Predicting Drug ADMET Properties Based on Graph Attention Network |
Gu Yaowen1,Zhang Bowen2,Zheng Si1,Yang Fengchun1,Li Jiao1( ) |
1Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing 100020, China 2XtalPi AI Research Center, Beijing 100089, China |
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Abstract [Objective] This study builds a prediction model for drugs’ ADMET properties (Absorption, Distribution, Metabolism, Excretion, Toxicity), aiming to evaluate drugs in virtual screening. [Methods] We constructed a drug ADMET prediction based on the Graph Attention Network (GAN). Then, we used the drug ADMET properties from open access databases and scientific publications to create their molecular graphs and structures. Finally, we compared the GAN-based model with three machine learning models and two graph neural network models. [Results] We collected 9 datasets with 149 457 ADMET records. The proposed prediction model had an average accuracy of 0.825 and an average F1-Score of 0.672 with the 9 datasets, which were 6.4% and 26.0% higher than those of the baseline models. [Limitations] The data cleansing process needs to be refined, while the prediction performance can be further improved with a pre-training architecture. [Conclusions] The proposed model could effectively predict a drug’s ADMET, which could help virtual drug screening and computer-aided drug developments.
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Received: 08 March 2021
Published: 15 September 2021
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Fund:National Natural Science Foundation of China(81601573);National Key Research and Development Program of China(2016YFC0901901) |
Corresponding Authors:
Li Jiao ORCID:0000-0001-6391-8343
E-mail: li.jiao@imicams.ac.cn
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