Machine learning-based prediction of drug-drug interactions for histamine antagonist using hybrid chemical features

Luong Huu Dang, Nguyen Tan Dung, Ly Xuan Quang, Le Quang Hung, Ngoc Hoang Le, Nhi Thao Ngoc Le, Nguyen Thi Diem, Nguyen Thi Thuy Nga, Shih Han Hung, Nguyen Quoc Khanh Le

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


The requesting of detailed information on new drugs including drug-drug interactions or targets is often unavailable and resource-intensive in assessing adverse drug events. To shorten the common evaluation process of drug-drug interactions, we present a machine learning framework-HAINI to predict DDI types for histamine antagonist drugs using simplified molecular-input line-entry systems (SMILES) combined with interaction features based on CYP450 group as inputs. The data used in our research consisted of approved drugs of histamine antagonists that are connected to 26,344 DDI pairs from the DrugBank database. Various classification algorithms such as Naive Bayes, Decision Tree, Random Forest, Logistic Regression, and XGBoost were used with 5-fold cross-validation to approach a large-scale DDIs prediction among histamine antagonist drugs. The prediction performance shows that our model outperformed previously published works on DDI prediction with the best precision of 0.788, a recall of 0.921, and an F1-score of 0.838 among 19 given DDIs types. An important finding of the study is that our prediction is based solely on the SMILES and CYP450 and thus can be applied at the early stage of drug development.

Original languageEnglish
Article number3092
Issue number11
Publication statusPublished - Nov 2021


  • Cheminformatics
  • Drug-drug interaction
  • Histamine antagonist
  • Machine learning
  • PyBioMed package

ASJC Scopus subject areas

  • General Medicine


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