Machine learning-based peptide material design strategies to enhance tumor targeting and therapy.

Abstract

Peptide materials have shown their great promise in precision cancer therapy due to their accurate target recognition ability, diverse anti-tumor mechanisms, and biocompatibility. However, conventional peptide discovery largely relies on trial-and-error methods, which are inherently limited by lengthy development cycles, inefficient exploration of the vast sequence space, and inadequate characterization of structure-activity relationships. Recent advances in machine learning have fundamentally transformed peptide material design by shifting the discovery paradigm from empirical trial-and-error to data-driven rational design. This review systematically summarizes the core framework of machine learning-assisted peptide material design, covering three core components: data acquisition, feature engineering, and model selection and training. We focus on the cutting-edge research progress of machine learning in enhancing the tumor targeting of peptide materials and developing new anti-tumor peptide materials, and introduce the professional databases and algorithm tools that support the development of this field. Finally, we discuss the major challenges facing in machine learning-driven design of tumor-targeted peptide materials, and suggest the future development direction in this field, aiming to provide a systematic theoretical reference for the rational design and clinical translation of novel tumor-targeted peptide materials.