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Yang K, Yang Y, Fan S, Xia J, Zheng Q, Dong X, Liu J, Liu Q, Lei L, Zhang Y, Li B, Gao Z, Zhang R, Liu B, Wang Z, Zhou X. DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning. Brief Bioinform 2023; 24:6958501. [PMID: 36562715 DOI: 10.1093/bib/bbac518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 12/24/2022] Open
Abstract
As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.
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Affiliation(s)
- Kuo Yang
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | | | - Shuyue Fan
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jianan Xia
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Qiguang Zheng
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Xin Dong
- Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Qiong Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Lei Lei
- Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Yingying Zhang
- Dongzhimen Hospital, Beijing University of Chinese Medicine, China
| | - Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, China
| | - Zhuye Gao
- Xiyuan Hospital, China Academy of Chinese Medical Sciences, National Clinical Research Center for Chinese Medicine Cardiology, China
| | - Runshun Zhang
- Guanganmen Hospital, China Academy of Chinese Medical Sciences, China
| | - Baoyan Liu
- Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, China
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, China
| | - Xuezhong Zhou
- Institute of Medical Intelligence, Beijing Key Lab of Traffic Data Analysis and Mining, School of Computer and Information Technology, Beijing Jiaotong University, China
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Dipeptidyl Peptidase-IV Blockers Potently Inhibit Monoglyceride Lipase: Investigation By Docking Studies And In Vitro Bioassay. Med Chem Res 2023. [DOI: 10.1007/s00044-022-02998-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
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Identification of potential COVID-19 main protease inhibitors using structure-based pharmacophore approach, molecular docking and repurposing studies. ACTA PHARMACEUTICA (ZAGREB, CROATIA) 2021; 71:163-174. [PMID: 33151166 DOI: 10.2478/acph-2021-0016] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/02/2020] [Indexed: 01/19/2023]
Abstract
The current outbreak of novel coronavirus (COVID-19) infections urges the need to identify potential therapeutic agents. Therefore, the repurposing of FDA-approved drugs against today's diseases involves the use of de-risked compounds with potentially lower costs and shorter development timelines. In this study, the recently resolved X-ray crystallographic structure of COVID-19 main protease (Mpro) was used to generate a pharmacophore model and to conduct a docking study to capture antiviral drugs as new promising COVID-19 main protease inhibitors. The developed pharmacophore successfully captured five FDA-approved antiviral drugs (lopinavir, remdesivir, ritonavir, saquinavir and raltegravir). The five drugs were successfully docked into the binding site of COVID-19 Mpro and showed several specific binding interactions that were comparable to those tying the co-crystallized inhibitor X77 inside the binding site of COVID-19 Mpro. Three of the captured drugs namely, remdesivir, lopinavir and ritonavir, were reported to have promising results in COVID-19 treatment and therefore increases the confidence in our results. Our findings suggest an additional possible mechanism of action for remdesivir as an antiviral drug inhibiting COVID-19 Mpro. Additionally, a combination of structure-based pharmacophore modeling with a docking study is expected to facilitate the discovery of novel COVID-19 Mpro inhibitors.
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