Мақола

Artificial intelligence‐driven anticancer peptide discovery

Junrui WuCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaShuaiqi JiCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaKashif Iqbal SahibzadaCollege of Biological Engineering Henan University of Technology Zhengzhou PR ChinaMengxue LouCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaFeiyu AnCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaWenqian LiCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaJiawei GuoCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaTaowei ZhangCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaXinyi ZhangCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaYilin ChouCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaHe-Nan ZhangCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR ChinaHao JinKey Laboratory of Dairy Biotechnology and Engineering, Ministry of Education Inner Mongolia Agricultural University Hohhot PR ChinaTeng MaKey Laboratory of Dairy Biotechnology and Engineering, Ministry of Education Inner Mongolia Agricultural University Hohhot PR ChinaWeichi LiuKey Laboratory of Dairy Biotechnology and Engineering, Ministry of Education Inner Mongolia Agricultural University Hohhot PR ChinaBegali AlikulovDepartment of Biotechnology Samarkand State University Samarkand UzbekistanNatalia Alekseevna GolovnevaInstitute of Microbiology of the National Academy of Sciences of Belarus Minsk BelarusHooi Ling FooDepartment of Bioprocess Technology, Faculty of Biotechnology and Biomolecular Sciences Universiti Putra Malaysia Serdang Selangor MalaysiaIssayeva KuralayDepartment of Biotechnology Toraighyrov University Pavlodar KazakhstanZhihong SunKey Laboratory of Dairy Biotechnology and Engineering, Ministry of Education Inner Mongolia Agricultural University Hohhot PR ChinaDong‐Qing WeiState Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai PR ChinaRina WuCollege of Food Science Shenyang Agricultural University, National Agricultural Environmental Microbial Germplasm Resource Bank, Liaoning Engineering Research Center of Food Fermentation Technology, Shenyang Key Laboratory of Microbial Fermentation Technology Innovation Shenyang PR China

iMetaOmics.journal2025en

ABI

Аннотация

Cancer has become a major global health threat. Despite advances in modern medicine, current therapeutic strategies still face many limitations. Anticancer peptides (ACPs), due to their high selectivity, low toxicity, and multitarget effects, have gradually become a research focus in the development of novel peptide-based anticancer drugs. However, traditional screening methods are constrained by their low efficiency, high costs, and technical complexity, limiting their capacity to meet the demands of high-throughput applications. Artificial intelligence (AI) has provided new methods to address these challenges, significantly improving the efficiency and accuracy of ACP screening through the application of machine learning and deep learning algorithms. To further enhance the application of AI in ACP screening, the advantages and limitations of 68 AI models used for ACP screening are systematically summarized. AI models show considerable potential for discovering ACPs, but most of these models lack interpretability and wet-laboratory validation, which hinder the credibility and practical effectiveness of AI-based ACP screening. Therefore, we presented a comprehensive ACP screening framework based on AI models. The presented framework includes data collection and organization, feature extraction, model construction, model interpretability analysis, and experimental validation. Additionally, we integrated this screening framework with multi-omics and other biotechnologies to promote the translation of AI-selected ACPs to the clinic. The presented AI-based ACP screening framework can accelerate the ACP development, increase ACP screening efficiency, and promote clinical ACP application.

Мавзулар

vaccines and immunoinformatics approaches Advanced Proteomics Techniques and Applications Machine Learning in Bioinformatics

Идентификаторлар

DOI: 10.1002/imo2.70063

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