Predicting Drug Target Interactions Based on GBDT

  • Jiyun Chen
  • Jihong Wang
  • Xiaodan Wang
  • Yingyi Du
  • Huiyou ChangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)


The research of the drug-target interactions (DTIs) is of great significance for drug development. Traditional chemical experiments are expensive and time-consuming. In recent years, many computational approaches based on different principles have been proposed gradually. Most of them use the information of drug-drug similarity and target-target similarity and made some progress. But the result is far from satisfactory. In this paper, we proposed machine learning method based on GBDT to predict DTIs with the IDs of both drug and protein, the descriptor of them, known DTIs and double negative samples. After gradient boosting and supervised training, GBDT construct decision trees for drug-target networks and generate precise model to predict new DTIs. Experimental results shows that Gradient Boosting Decision Tree (GBDT) reaches or outperforms other state-of-the-art methods.


Drug-target interaction prediction DTIs Drug discovery Machine learning GBDT DrugBank 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Jiyun Chen
    • 1
  • Jihong Wang
    • 1
  • Xiaodan Wang
    • 1
  • Yingyi Du
    • 1
  • Huiyou Chang
    • 1
    Email author
  1. 1.School of Data and Computer ScienceSun Yat-sen UniversityGuangzhouChina

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