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

Area of Science:

• Biomaterials Science
• Computational Biology
• Oncology

Background:

• Peptide materials offer precise cancer targeting and biocompatibility for therapy.
• Traditional peptide discovery is slow, inefficient, and lacks structure-activity insights.
• Machine learning (ML) revolutionizes peptide design from empirical methods to data-driven approaches.

Purpose of the Study:

• To review the framework of ML-assisted peptide material design for cancer therapy.
• To highlight ML advancements in enhancing tumor targeting and developing anti-cancer peptides.
• To provide a reference for rational design and clinical translation of targeted peptide materials.

Main Methods:

• Systematic review of ML applications in peptide material design.
• Focus on data acquisition, feature engineering, and model selection/training.
• Introduction of relevant databases and algorithms for peptide development.

Main Results:

• ML enables rational design, overcoming limitations of trial-and-error methods.
• Significant progress in using ML to improve tumor targeting of peptide materials.
• Development of new anti-tumor peptide materials is accelerated by ML.

Conclusions:

• ML-driven design offers a powerful paradigm shift for peptide material development.
• Addressing challenges in ML-driven design will facilitate clinical translation.
• Future directions focus on optimizing ML models for novel cancer therapies.