Recent advances in multimodal foundation model-enabled peptide screening and optimization for smart biomaterials and functional tissue engineering
Abstract
Peptides are important bioactive molecules for smart biomaterials and functional tissue engineering because they can provide targeting, antimicrobial, adhesive, immunomodulatory, and differentiation-regulating functions. However, conventional peptide discovery remains limited by high experimental cost, incomplete exploration of sequence space, and the difficulty of balancing bioactivity with stability, safety, manufacturability, and material compatibility. These limitations become more pronounced when peptides must retain function after chemical modification, immobilization, hydrogel incorporation, crosslinking, printing, or construct maturation. Recent advances in protein language models and multimodal foundation models provide new opportunities for peptide screening, generation, property prediction, and material-aware prioritization. This review summarizes foundation model-assisted strategies for peptide representation learning, candidate retrieval, de novo generation, interaction and property prediction, multi-objective optimization, benchmarking, developability assessment, and staged material-level validation. We also discuss representative applications in antimicrobial biomaterials, targeting systems, immunomodulatory materials, peptide-functionalized hydrogels, hydrogel bioinks, and three-dimensional bioprinted constructs. Overall, foundation model-assisted peptide design should be viewed as a candidate-prioritization framework rather than a substitute for experimental…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.