Hollow CoFe Nanozymes Integrated with Oncolytic Peptides Designed via Machine‐Learning for Tumor Therapy

Abstract

Developing novel substances to synergize with nanozymes is a challenging yet indispensable task to enable the nanozyme‐based therapeutics to tackle individual variations in tumor physicochemical properties. The advancement of machine learning (ML) has provided a useful tool to enhance the accuracy and efficiency in developing synergistic substances. In this study, ML models to mine low‐cytotoxicity oncolytic peptides are applied. The filtering Pipeline is constructed using a traversal design and the Autogluon framework. Through the Pipeline, 37 novel peptides with high oncolytic activity against cancer cells and low cytotoxicity to normal cells are identified from a library of 25,740 sequences. Combining dataset testing with cytotoxicity experiments, an 80% accuracy rate is achieved, verifying the reliability of ML predictions. Peptide C2 is proven to possess membranolytic functions specifically for tumor cells as targeted by Pipeline. Then Peptide C2 with CoFe hollow hydroxide nanozyme (H‐CF) to form the peptide/H‐CF composite is integrated. The new composite exhibited acid‐triggered membranolytic function and potent peroxidase‐like (POD‐like) activity, which induce ferroptosis to tumor cells and inhibits tumor growth. The study suggests that this novel ML‐assisted design approach can offer an accurate and efficient paradigm for developing both oncolytic peptides and synergistic peptides for catalytic materials.

Unleashing the Potential of Single-Atom Nanozymes: Catalysts for the Future

“…39 The core concepts of ML allow it to discover hidden patterns within the complex interconnections between the structure and performance of SANs, even in cases when these connections are unclear. 40 By employing experimental data to improve the specificity of catalysis, as well as utilizing data mining and machine learning for performance prediction, we may develop a powerful tool for making progressive enhancements. Nevertheless, it is crucial to acknowledge that ML in nanozyme research is now in its early stages and primarily depends on data obtained from SANs databases, which is still limited.…”

“…Furthermore, machine learning (ML), a branch of artificial intelligence renowned for effectively managing large data sets, may offer some insights for enhanced selectivity of SANs . The core concepts of ML allow it to discover hidden patterns within the complex interconnections between the structure and performance of SANs, even in cases when these connections are unclear . By employing experimental data to improve the specificity of catalysis, as well as utilizing data mining and machine learning for performance prediction, we may develop a powerful tool for making progressive enhancements.…”