| 人工智能方法在抗菌肽筛选领域的应用及展望 |
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| 引用本文： | 薛凤,封硕,李菁.人工智能方法在抗菌肽筛选领域的应用及展望[J].中国药科大学学报,2023,54(3):314-322. |
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| 作者姓名： | 薛凤 封硕 李菁 |
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| 作者单位： | 中国药科大学生命科学与技术学院,中国药科大学生命科学与技术学院,中国药科大学生命科学与技术学院;中国药科大学药物质量与安全预警教育部重点实验室 |
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| 基金项目： | 国家自然科学基金资助项目（No.32170062）；江苏省研究生科研与实践创新计划资助项目（No.3322200020） |
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| 摘 要： | 抗菌肽（antimicrobial peptides，AMPs）是一类具有广谱抗菌活性的小分子肽，其独特抗菌机制能够有效治疗感染性疾病，且不易产生耐药性。然而，利用传统实验方式虽然能够筛选出具有抗菌活性的AMPs，但是筛选过程繁琐，人工智能筛选方法则更加快捷便利，在探索新型天然抗菌肽中展现了巨大的潜力。本文总结并比较了人工智能筛选AMPs的相关策略，包括应用于模型训练的数据来源、人工智能机器模型以及应用于模型筛选新型抗菌肽的组学数据，并对应用的前景和优势进行展望，以期为抗菌肽的鉴定识别、研发改造提供新思路。
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| 关 键 词： | 人工智能 机器学习 深度学习 抗菌肽 |
| 收稿时间： | 2023/3/9 0:00:00 |
| 修稿时间： | 2023/6/12 0:00:00 |
Application and prospect of artificial intelligence in antimicrobial peptides screening |
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| XUE Feng,FENG Shuo and LI Jing.Application and prospect of artificial intelligence in antimicrobial peptides screening[J].Journal of China Pharmaceutical University,2023,54(3):314-322. |
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| Authors: | XUE Feng FENG Shuo and LI Jing |
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| Affiliation: | School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009,School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009,School of Life Science and Technology, China Pharmaceutical University, Nanjing 210009;Ministry of Education Key Laboratory of Drug Quality Control and Pharmacovigilance, China Pharmaceutical University, Nanjing 210009, China |
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| Abstract: | Antimicrobial peptides (AMPs) are a class of small molecule peptides with broad-spectrum antimicrobial activity.Their unique antimicrobial mechanism can effectively treat infectious diseases, with rare drug resistance.However, though AMPs with antimicrobial activity can be screened by traditional methods, the whole process is complicated.The artificial intelligence (AI) screening method is faster and more convenient, with great potential in exploring new natural antimicrobial peptides.In this paper, strategies related to AMPs screening by AI were summarized and compared, including data sources applied to model training, artificial intelligence machine model and omics data applied to model screening of novel antimicrobial peptides.The application prospects and advantages were reviewed, in hope of providing new ideas for identification, research and development of antimicrobial peptides. |
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| Keywords: | artificial intelligence machine learning deep learning antimicrobial peptides |
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