On searching antimicrobial agents among natural products:Fighting against Superbug
Project/Area Number |
20K12043
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Review Section |
Basic Section 62010:Life, health and medical informatics-related
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Research Institution | Nara Institute of Science and Technology |
Principal Investigator |
AMIN MD.ALTAFUL 奈良先端科学技術大学院大学, 先端科学技術研究科, 准教授 (30379531)
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Project Period (FY) |
2020-04-01 – 2024-03-31
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Project Status |
Granted (Fiscal Year 2022)
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Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2022: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2021: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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Keywords | Antibiotic compounds / Natural products / Machine learning / Jamu formulas / TCM formulas / Antimicrobial / Traditional Medicines / Lasso regression / Deep learning / Machine Learning / Graph Clustering / Random Forest / Chemoinformatics / Antimicrobials / Natural Products |
Outline of Research at the Start |
Antimicrobial agents are drugs that can kill microorganisms or stop their growth. Widespread overdose and irresponsible usage of antibiotics in clinical practicees for both human and livestock has resulted in resistance of bacteria to antimicrobial agents. Such multidrug-resistant (MDR) bacteria are recently called as Superbugs. MDR bacteria poses global problems with the threat of the reoccurrence of a situation of the pre-antibiotic era and increased cost of healthcare services. This work will search antimicrobial agents/drugs among natural products by utilizing mainly chemoinformatics.
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Outline of Annual Research Achievements |
Antimicrobial agents are drugs that can kill microorganisms or stop their growth. massive imprudent usage of antibiotics in clinical practice for both human and livestock has resulted in resistance of bacteria to antimicrobial agents. Our research focused on finding natural antibiotic compounds based on traditional medicine formulas. We applied various machine learning algorithms such as Lasso regression, Random Forest and XGBoost, deep learning to Jamu and TCM formulas aiming to finding natural antibiotic plants and compounds. Based on our research in the current fiscal year, we published two papers in journals with reasonable impact factors. One of the papers published in the journal Antibiotics (Impact Factor 4.94) identified antibiotic plants based on Jamu formulas. Another paper published in the journal Methods (Impact Factor 4.647) found out antibacterial natural compounds based on TCM formulas. We also published two papers in IEEE conferences.
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Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
Our PhD students Mr. Ahmad Kamal Nasution and Mr. Gao Pei helped me a lot for conducting this research
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Strategy for Future Research Activity |
Currently we are working on finding natural antibiotic compounds based on Unani formulas. Next, we want make comparison between the results we obtained based Jamu, TCM and Unani formulas. We will make a comprehensive list of promising natural antibiotic compounds and by applying unsupervised clustering we will try to identify the type of mechanisms of actions against bacteria.
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Report
(3 results)
Research Products
(17 results)
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[Journal Article] On Finding Natural Antibiotics based on TCM Formulae2023
Author(s)
Gao, P., Nasution, A. K., Yang, S., Chen, Z., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M. D
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Journal Title
Methods
Volume: -
Pages: 35-45
DOI
Related Report
Int'l Joint Research
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[Journal Article] Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach2021
Author(s)
Wijaya, S. H., Afendi, F. M., Batubara, I., Huang, M., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M
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Journal Title
life
Volume: 11(8)
Issue: 8
Pages: 866-866
DOI
Related Report
Int'l Joint Research
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[Presentation] An Integrated Multi-Omics Approach for AMR Phenotype Prediction of Gut Microbiota2021
Author(s)
Gao, P., Chen, Z., Wang, D., Huang, M., Ono, N., Altaf-Ul-Amin, M., & Kanaya, S
Organizer
IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Related Report
Int'l Joint Research
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[Presentation] Prediction of TCM Effective against Bacterial Pneumonia and Identification of Antibacterial Natural Product2021
Author(s)
Gao, P., Chen, Z., Huang, M., Ono, N., Amin, A., & Kanaya, S
Organizer
The 43rd Annual International Conference of the IEEE Engineering in Medicine and Biology Societye (EMBC)
Related Report
Int'l Joint Research
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