| 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 |
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| Section | 一般 |
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| Review Section |
Basic Section 62010:Life, health and medical informatics-related
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| Research Institution | Nara Institute of Science and Technology |
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Principal Investigator |
Amin Md Altaf Ul 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 |
Completed (Fiscal Year 2023)
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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 | Natural antibiotics / Traditional medicines / Machine learning / Jamu formulas / TCM formulas / Antibiotic compounds / Natural products / Antimicrobial / Traditional Medicines / Lasso regression / Deep learning / Machine Learning / Graph Clustering / Random Forest / Chemoinformatics / Antimicrobials / Natural Products |
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| 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 Final Research Achievements |
Antibiotic resistance is a major public health threat and there is an urgent need for new antibiotics. Traditional herbal medicine systems, such as Jamu, Unani, and Traditional Chinese Medicine, have been used for finding new antibiotics by applying machine learning algorithms. In total, we predicted 42 potential plant candidates and 201 candidate metabolites as potential natural antibiotics. We published 4 journal papers (with IF > 4) and two IEEE conference papers using the results of this research. With this KAKENHI money we also conducted some other related researches.
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| Academic Significance and Societal Importance of the Research Achievements |
Our research focused on finding natural antibiotic compounds based on traditional medicine formulas by applying various machine learning algorithms. By further investigation if some of our predicted antibiotics can be used in clinical practice it would be of great scientific and social significance.
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