Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery
Abstract Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterog...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-82498-1 |
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| author | Yingcan Li Yu Shen Yezi Cai Yulin zhang Jiahui Gao Lei Huang Weinuo Si Kai Zhou Shan Gao Qichao Luo |
| author_facet | Yingcan Li Yu Shen Yezi Cai Yulin zhang Jiahui Gao Lei Huang Weinuo Si Kai Zhou Shan Gao Qichao Luo |
| author_sort | Yingcan Li |
| collection | DOAJ |
| description | Abstract Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP . |
| format | Article |
| id | doaj-art-c38ca5869dfc4637b29ffded1352b1e4 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c38ca5869dfc4637b29ffded1352b1e42025-08-20T02:59:24ZengNature PortfolioScientific Reports2045-23222025-03-0115111510.1038/s41598-024-82498-1Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discoveryYingcan Li0Yu Shen1Yezi Cai2Yulin zhang3Jiahui Gao4Lei Huang5Weinuo Si6Kai Zhou7Shan Gao8Qichao Luo9Department of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityResearch Center for Neurological Disorders, School of Basic Medicine, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityDepartment of Pharmacology, Basic Medical College, Anhui Medical UniversityAbstract Identifying the active natural compounds remains a challenge for drug discovery, and new algorithms need to be developed to predict active ingredients from complex natural products. Here, we proposed Meta-DEP, a Meta-paths-based Drug Efficacy Prediction based on drug-protein-disease heterogeneity network, where Meta-paths contain all the shortest paths between drug targets and disease-related proteins in the network and drug efficacy is measured by a predictive score according to drug disease network proximity. Experiments show that Meta-DEP performs better than traditional network topology analysis on drug-disease interaction prediction task. Further investigations demonstrate that the key targets identified by Meta-DEP for drug efficacy are consistent with clinical pharmacological evidence. To prove that Meta-DEP can be used to discover active natural compounds, we apply it to predict the relationship between the monomeric components of traditional Chinese medicine included in the TCMSP database and diseases. Results indicate that Meta-DEP can accurately predict most of the drug-disease pairs included in the TCMSP database. In addition, biological experiments are directly used to demonstrate that Meta-DEP can mined active compound from traditional Chinese medicine with integrating disease transcriptomic data. Overall, the model developed in this study provides new impetus for driving the natural compound into innovative lead molecule. Code and data are available at https://github.com/t9lex/Meta-DEP .https://doi.org/10.1038/s41598-024-82498-1Drug efficacy predictionDrug-protein-disease networkDisease transcriptomic dataDeep learningTraditional Chinese medicine |
| spellingShingle | Yingcan Li Yu Shen Yezi Cai Yulin zhang Jiahui Gao Lei Huang Weinuo Si Kai Zhou Shan Gao Qichao Luo Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery Scientific Reports Drug efficacy prediction Drug-protein-disease network Disease transcriptomic data Deep learning Traditional Chinese medicine |
| title | Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery |
| title_full | Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery |
| title_fullStr | Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery |
| title_full_unstemmed | Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery |
| title_short | Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery |
| title_sort | integrating transcriptomic data with a novel drug efficacy prediction model for tcm active compound discovery |
| topic | Drug efficacy prediction Drug-protein-disease network Disease transcriptomic data Deep learning Traditional Chinese medicine |
| url | https://doi.org/10.1038/s41598-024-82498-1 |
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