An AI-based Prediction Model for Drug-drug Interactions in Osteoporosis and Paget's Diseases from SMILES

Truong Nguyen Khanh Hung, Nguyen Quoc Khanh Le, Ngoc Hoang Le, Le Van Tuan, Thuan Phuoc Nguyen, Cao Thi, Jiunn Horng Kang

研究成果: 雜誌貢獻文章同行評審

4 引文 斯高帕斯(Scopus)


The skeleton is one of the most important organs in the human body in assisting our motion and activities; however, bone density attenuates gradually as we age. Among common bone diseases are osteoporosis and Paget's, two of the most frequently found diseases in the elderly. Nowadays, a combination of multiple drugs is the optimal therapy to decelerate osteoporosis and Paget's pathologic process, which comes with various underlying adverse effects due to drug-drug interactions (DDIs). Artificial intelligence (AI) has the potential to evaluate the interaction, pharmacodynamics, and possible side effects between drugs. In this research, we created an AI-based machine-learning model to predict the outcomes of interactions between drugs used for osteoporosis and Paget's treatment, which helps mitigate the cost and time to implement the best combination of medications in clinical practice. In this study, a DDI dataset was collected from the DrugBank database within the osteoporosis and Paget diseases. We then extracted a variety of chemical features from the simplified molecular-input line-entry system (SMILES) of defined drug pairs that interact with each other. Finally, machine-learning algorithms were implemented to learn the extracted features. Our stack ensemble model from Random Forest and XGBoost reached an average accuracy of 74 % in predicting DDIs. It was superior to individual models as well as previous methods in terms of most measurement metrics. This study showed the potential of AI models in predicting DDIs of Osteoporosis-Paget's disease in particular, and other diseases in general.

期刊Molecular Informatics
出版狀態接受/付印 - 2022

ASJC Scopus subject areas

  • 結構生物學
  • 分子醫學
  • 藥物發現
  • 電腦科學應用
  • 有機化學


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