On searching antimicrobial agents among natural products:Fighting against Superbug

天然产物中寻找抗菌药物：对抗超级细菌

• 批准号: 20K12043
• 负责人: AMIN MD.ALTAFUL
• 金额: $ 2.08万
• 依托单位: Nara Institute of Science and Technology
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2020
• 资助国家: 日本
• 起止时间: 2020-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-20K12043/
• 关键词: 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

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.

抗菌剂是可以杀死微生物或阻止其生长的药物。在人类和牲畜的临床实践中，大量不谨慎地使用抗生素已导致细菌对抗菌药物产生耐药性。我们的研究重点是寻找基于传统药物配方的天然抗生素化合物。我们将Lasso回归、Random Forest和XGBoost等多种机器学习算法，深度学习应用于Jamu和中药配方，旨在寻找天然抗生素植物和化合物。基于本财年的研究，我们在影响因子合理的期刊上发表了两篇论文。发表在《抗生素》（影响因子4.94）杂志上的一篇论文根据Jamu配方确定了抗生素植物。另一篇发表在《方法》杂志（影响因子4.647）上的论文发现了基于中药配方的抗菌天然化合物。我们还在IEEE会议上发表了两篇论文。

项目成果

Recent Trends in Computational Research on Diseases

疾病计算研究的最新趋势

• 发表时间: 2022
• 作者: Md. Altaf-Ul-Amin;Shigehiko Kanaya;Naoaki Ono and Ming Huang (Eds.)
• 通讯作者: Naoaki Ono and Ming Huang (Eds.)

DPClusSBO: An integrated software for clustering of simple and bipartite graphs

• DOI: 10.1016/j.softx.2021.100821
• 发表时间: 2021-09-30
• 期刊: SOFTWAREX
• 影响因子: 3.4
• 作者: Karim，Mohammad Bozlul;Kanaya，Shigehiko;Altaf-Ul-Amin，Md
• 通讯作者: Altaf-Ul-Amin，Md

An Integrated Multi-Omics Approach for AMR Phenotype Prediction of Gut Microbiota

肠道微生物群 AMR 表型预测的综合多组学方法

• 发表时间: 2021
• 作者: Gao;P.;Chen;Z.;Wang;D.;Huang;M.;Ono;N.;Altaf-Ul-Amin;M.;& Kanaya;S
• 通讯作者: S

Prediction of TCM Effective against Bacterial Pneumonia and Identification of Antibacterial Natural Product

中药治疗细菌性肺炎疗效预测及抗菌天然产物鉴定

• 发表时间: 2021
• 作者: Gao;P.;Chen;Z.;Huang;M.;Ono;N.;Amin;A.;& Kanaya;S
• 通讯作者: S

Novel Methods and Tool for Clustering of Simple and Bipartite Graphs: Applications in Ecology and Computational Biomedical Research

简单图和二部图聚类的新方法和工具：在生态学和计算生物医学研究中的应用

• 发表时间: 2023
• 作者: Kazuyuki Matsumoto;Matsunaga Takumi;Minoru Yoshida;Kenji Kita;柾凱斗，神谷幸宏;Md. Altaf-Ul-Amin
• 通讯作者: Md. Altaf-Ul-Amin