2020 Fiscal Year Research-status Report
On searching antimicrobial agents among natural products:Fighting against Superbug
| Project/Area Number |
20K12043
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| Research Institution | Nara Institute of Science and Technology |
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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 – 2023-03-31
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| Keywords | Antimicrobial / Natural products / Traditional Medicines / Lasso regression / Random Forest / Machine Learning |
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| Outline of Annual Research Achievements |
Chemoinformatics has become greatly involved with modern drug discovery processes. 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. Such multidrug-resistant (MDR) bacteria are recently called as Superbugs. MDR bacteria poses global threats to human health and economy. Antimicrobials work based on multiple mechanisms and multiple drugs are necessary to treat microbial infections effectively. But multiple drugs cause lots of side effects. Traditional medicines are known to have no or less side effects. Therefore, good combination of multiple antibiotic drugs derived from natural products might be a good solution to combat superbug. Because of the covid pandemic we are focusing on finding natural antibiotics effective against virus and bacteria that cause respiratory diseases. We have collected around 10000 Traditional Chinese Medicine (TCM) Formulas. We developed an approach to construct and validate TCM dataset effective against bacterial pneumonia and published a conference paper. Currently we are working on Identification of Antibacterial Natural Products based on appropriate TCM formulas and computational techniques using on machine learning algorithms. We will apply Lasso regression, Random Forest and XGBoost algorithms and utilize the best classification/regression results to identify natural product antibiotics.
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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
I have been conducting research on traditional medicibes for a decade. Previously I worked on Indonesian Jamu and sub-continental traditional medicines such as Ayurvedic and Unani. My experience is helping to conduct the research of the current project. Also, a graduate student of our lab is working with me. With his help we are conducting our research and experiments smoothly. We have collected around 10000 Traditional Chinese Medicine (TCM) Formulas. We developed an approach to construct and validate TCM dataset effective against bacterial pneumonia and published a conference paper. Currently we are working on Identification of Antibacterial Natural Products based on the selected TCM formulas by applying computational techniques. We have already done part of the work and will write a paper soon.
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| Strategy for Future Research Activity |
Our next work is Identification of Antibacterial Natural Products based on appropriate TCM formulas using computational approaches based on machine learning algorithms. We will apply Lasso regression:, Random Forest and XGBoost algorithms and utilize the best classification/regression results to identify natural product antibiotics.
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| Causes of Carryover |
This year we could not attend a conference because of the pandemic. This year we hope in the second part we will be able to attend some conferences domestic or overseas. By attending conferences we can gather feedback on our work and new research ideas.
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