Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning
The problem of bacterial resistance poses a significant threat to human and animal health as well as public safety, making the discovery of effective new antimicrobial compounds an urgent priority. Traditional methods for screening antimicrobial activity are often time-consuming and labor-intensive,...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
China Food Publishing Company
2025-07-01
|
| Series: | Shipin Kexue |
| Subjects: | |
| Online Access: | https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-14-039.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849470545715789824 |
|---|---|
| author | HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi |
| author_facet | HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi |
| author_sort | HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi |
| collection | DOAJ |
| description | The problem of bacterial resistance poses a significant threat to human and animal health as well as public safety, making the discovery of effective new antimicrobial compounds an urgent priority. Traditional methods for screening antimicrobial activity are often time-consuming and labor-intensive, with limited accuracy and objectivity. As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. This paper reviews commonly used machine learning models, such as random forests, support vector machines, and deep learning, in antimicrobial activity screening. It provides an in-depth exploration of machine learning applications in the discovery of antimicrobial peptides, essential oils, and polyphenols, aiming to offer valuable insights into the application of machine learning techniques for identifying antimicrobial compounds. |
| format | Article |
| id | doaj-art-ee268b4fff884eb8b89b4bb5ef5b3364 |
| institution | Kabale University |
| issn | 1002-6630 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | China Food Publishing Company |
| record_format | Article |
| series | Shipin Kexue |
| spelling | doaj-art-ee268b4fff884eb8b89b4bb5ef5b33642025-08-20T03:25:07ZengChina Food Publishing CompanyShipin Kexue1002-66302025-07-01461436635610.7506/spkx1002-6630-20241130-213Research Progress in the Screening of Antimicrobial Substances Based on Machine LearningHOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi0(1. College of Biology and Food Engineering, Hubei Minzu University, Enshi 445000, China; 2. Institute of Agro-product Processing and Nuclear Agricultural Technology, Wuhan 430064, China; 3. Wuhan Liangzi Lake Aquatic Products Processing Co., Ltd., Wuhan 430212, China)The problem of bacterial resistance poses a significant threat to human and animal health as well as public safety, making the discovery of effective new antimicrobial compounds an urgent priority. Traditional methods for screening antimicrobial activity are often time-consuming and labor-intensive, with limited accuracy and objectivity. As a branch of artificial intelligence, machine learning algorithms have demonstrated exceptional capabilities in processing large-scale data, feature extraction, and model optimization, leading to their increasing application in the screening of antimicrobial substances. This paper reviews commonly used machine learning models, such as random forests, support vector machines, and deep learning, in antimicrobial activity screening. It provides an in-depth exploration of machine learning applications in the discovery of antimicrobial peptides, essential oils, and polyphenols, aiming to offer valuable insights into the application of machine learning techniques for identifying antimicrobial compounds.https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-14-039.pdfmachine learning; antimicrobial substances; screening |
| spellingShingle | HOU Jiangxia, JIANG Jinhui, WANG Chenxin, WANG Lan, SHI Liu, WU Wenjin, GUO Xiaojia, CHEN Sheng, CHEN Lang, CAO Feng, SUN Li, ZHOU Zhi Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning Shipin Kexue machine learning; antimicrobial substances; screening |
| title | Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning |
| title_full | Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning |
| title_fullStr | Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning |
| title_full_unstemmed | Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning |
| title_short | Research Progress in the Screening of Antimicrobial Substances Based on Machine Learning |
| title_sort | research progress in the screening of antimicrobial substances based on machine learning |
| topic | machine learning; antimicrobial substances; screening |
| url | https://www.spkx.net.cn/fileup/1002-6630/PDF/2025-46-14-039.pdf |
| work_keys_str_mv | AT houjiangxiajiangjinhuiwangchenxinwanglanshiliuwuwenjinguoxiaojiachenshengchenlangcaofengsunlizhouzhi researchprogressinthescreeningofantimicrobialsubstancesbasedonmachinelearning |