Artificial intelligence-driven anticancer peptide discovery

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.

Text 122229.pdf - Published Version Available under License Creative Commons Attribution. Download (5MB) Official URL or Download Paper: https://onlinelibrary.wiley.com/doi/10.1002/imo2.7...

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