Xiangxiang Zeng1, Siyi Zhu2, Yuan Hou3, Pengyue Zhang4, Lang Li4, Jing Li5, L Frank Huang6,7, Stephen J Lewis8, Ruth Nussinov9,10, Feixiong Cheng3,11,12. 1. Department of Computer Science, College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China. 2. Department of Computer Science, Xiamen University, Xiamen 361005, China. 3. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA. 4. Department of Biomedical Informatics, Ohio State University, Columbus, OH 43210, USA. 5. Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA. 6. Division of Experimental Hematology and Cancer Biology, Brain Tumor Center, Cincinnati Children's Hospital Medical Center. 7. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA. 8. Department of Pediatrics, Case Western Reserve University, Cleveland, OH 44106, USA. 9. Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD 21702, USA. 10. Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel. 11. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA. 12. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
Abstract
MOTIVATION: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS: In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.
MOTIVATION: Systematic identification of molecular targets among known drugs plays an essential role in drug repurposing and understanding of their unexpected side effects. Computational approaches for prediction of drug-target interactions (DTIs) are highly desired in comparison to traditional experimental assays. Furthermore, recent advances of multiomics technologies and systems biology approaches have generated large-scale heterogeneous, biological networks, which offer unexpected opportunities for network-based identification of new molecular targets among known drugs. RESULTS: In this study, we present a network-based computational framework, termed AOPEDF, an arbitrary-order proximity embedded deep forest approach, for prediction of DTIs. AOPEDF learns a low-dimensional vector representation of features that preserve arbitrary-order proximity from a highly integrated, heterogeneous biological network connecting drugs, targets (proteins) and diseases. In total, we construct a heterogeneous network by uniquely integrating 15 networks covering chemical, genomic, phenotypic and network profiles among drugs, proteins/targets and diseases. Then, we build a cascade deep forest classifier to infer new DTIs. Via systematic performance evaluation, AOPEDF achieves high accuracy in identifying molecular targets among known drugs on two external validation sets collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868] and ChEMBL (AUROC = 0.768) databases, outperforming several state-of-the-art methods. In a case study, we showcase that multiple molecular targets predicted by AOPEDF are associated with mechanism-of-action of substance abuse disorder for several marketed drugs (such as aripiprazole, risperidone and haloperidol). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/AOPEDF.
Authors: Tina Hernandez-Boussard; Michelle Whirl-Carrillo; Joan M Hebert; Li Gong; Ryan Owen; Mei Gong; Winston Gor; Feng Liu; Chuong Truong; Ryan Whaley; Mark Woon; Tina Zhou; Russ B Altman; Teri E Klein Journal: Nucleic Acids Res Date: 2007-11-21 Impact factor: 16.971
Authors: M Babul Islam; Utpala Nanda Chowdhury; Zulkar Nain; Shahadat Uddin; Mohammad Boshir Ahmed; Mohammad Ali Moni Journal: Comput Biol Med Date: 2021-07-23 Impact factor: 4.589