Large-Scale Prediction of Beneficial Drug Combinations Using Drug Efficacy and Target Profiles

Hiroaki Iwata, Ryusuke Sawada, Sayaka Mizutani, Masaaki Kotera, Yoshihiro Yamanishi

研究成果: ジャーナルへの寄稿学術誌査読

20 被引用数 (Scopus)


The identification of beneficial drug combinations is a challenging issue in pharmaceutical and clinical research toward combinatorial drug therapy. In the present study, we developed a novel computational method for large-scale prediction of beneficial drug combinations using drug efficacy and target profiles. We designed an informative descriptor for each drug-drug pair based on multiple drug profiles representing drug-targeted proteins and Anatomical Therapeutic Chemical Classification System codes. Then, we constructed a predictive model by learning a sparsity-induced classifier based on known drug combinations from the Orange Book and KEGG DRUG databases. Our results show that the proposed method outperforms the previous methods in terms of the accuracy of high-confidence predictions, and the extracted features are biologically meaningful. Finally, we performed a comprehensive prediction of novel drug combinations for 2,639 approved drugs, which predicted 142,988 new potentially beneficial drug-drug pairs. We showed several examples of successfully predicted drug combinations for a variety of diseases.

ジャーナルJournal of Chemical Information and Modeling
出版ステータス出版済み - 12月 28 2015

!!!All Science Journal Classification (ASJC) codes

  • 化学 (全般)
  • 化学工学(全般)
  • コンピュータ サイエンスの応用
  • 図書館情報学


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