research-article

Classification and prediction model of compound pharmacokinetic properties based on ensemble learning method

Authors:

Jiayi Zhao,

Yang LiuAuthors Info & Claims

ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

Pages 526 - 531

https://doi.org/10.1145/3500931.3501021

Published: 22 December 2021 Publication History

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Abstract

In this paper, the absorption, distribution, metabolism, excretion, and toxicity of compounds are modeled, and the classification prediction models of Caco-2, CYP3A4, HERG, hob and Mn in ADMET properties are constructed respectively. Firstly, the main variables corresponding to the five indicators are obtained and the special data set is constructed. Then, two sets of integrated learning schemes, bagging integrated decision tree and boosting integrated GBDT, are used for modeling. At the same time, logical regression and naive Bayesian algorithm is used for classification prediction as the control group to construct the classification model. Finally, ACC, F1 and other indexes are used as model evaluation indexes to select the optimal model of each index. The results show that the characteristic distributions of Mn and HERG, Caco-2, CYP3A4 and HOB are similar.

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Cited By

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Tran TTayara HChong K(2023)Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future PerspectivesPharmaceutics10.3390/pharmaceutics1504126015:4(1260)Online publication date: 17-Apr-2023
https://doi.org/10.3390/pharmaceutics15041260

Index Terms

Classification and prediction model of compound pharmacokinetic properties based on ensemble learning method
1. Computing methodologies
  1. Modeling and simulation
    1. Model development and analysis
      1. Modeling methodologies
    2. Simulation evaluation

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ISAIMS '21: Proceedings of the 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 2021

593 pages

ISBN:9781450395588

DOI:10.1145/3500931

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2021

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ISAIMS 2021

ISAIMS 2021: 2nd International Symposium on Artificial Intelligence for Medicine Sciences

October 29 - 31, 2021

Beijing, China

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Overall Acceptance Rate 53 of 112 submissions, 47%

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Tran TTayara HChong K(2023)Artificial Intelligence in Drug Metabolism and Excretion Prediction: Recent Advances, Challenges, and Future PerspectivesPharmaceutics10.3390/pharmaceutics1504126015:4(1260)Online publication date: 17-Apr-2023
https://doi.org/10.3390/pharmaceutics15041260

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