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Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and
Chen Ding1, Yuxi Luo2, Ximiao Yu3,4
1Department of Pulmonary and Critical Care Medicine, The Affiliated Wuxi No. 2 People's Hospital of Nanjing Medical University, Wuxi, China.
Frontiers in Bioengineering and Biotechnology
|August 13, 2026
Summary
Foundation models accelerate peptide discovery for biomaterials and tissue engineering by improving screening, generation, and property prediction. These AI tools aid in designing functional peptides while considering material compatibility and stability.
Area of Science:
- Biomaterials Science
- Bioengineering
- Computational Biology
Background:
- Peptides are crucial bioactive molecules for advanced biomaterials and tissue engineering, offering functions like targeting, antimicrobial activity, and immunomodulation.
- Conventional peptide discovery faces challenges including high costs, limited sequence exploration, and difficulties in balancing bioactivity with material compatibility and stability.
- These limitations are amplified when peptides require modification or integration into complex material systems like hydrogels or 3D printed constructs.
Purpose of the Study:
- To review foundation model-assisted strategies for peptide design in biomaterials and tissue engineering.
- To explore applications of these models in areas such as antimicrobial biomaterials, targeting systems, and 3D bioprinting.
- To discuss the potential and limitations of foundation models in peptide discovery and material integration.
Main Methods:
- Summarizing foundation model applications in peptide representation learning, de novo generation, and property prediction.
- Reviewing material-aware prioritization strategies for peptide candidates.
- Discussing model-assisted benchmarking, developability assessment, and material-level validation.
Main Results:
- Foundation models offer new opportunities for efficient peptide screening, generation, and property prediction.
- These models facilitate the design of peptides with tailored functions for specific material applications.
- The review covers diverse applications, including antimicrobial materials, hydrogels, and 3D bioprinted constructs.
Conclusions:
- Foundation model-assisted peptide design serves as a powerful candidate-prioritization framework, complementing experimental validation.
- Future advancements require standardized material-context data, transparent benchmarking, and validation across multiple settings.
- These AI-driven approaches hold significant promise for advancing smart biomaterials and functional tissue engineering.
