Review highlights advances in AI-driven multiscale protein design, suggesting closed-loop generative workflows accelerate programmable biomaterial discovery.
Key Points
To review artificial intelligence frameworks that enable the physics-informed, multiscale design of functional protein-based materials from molecular sequences to macroscopic structures.
Surveyed machine-learning architectures capable of encoding biophysical principles across multiple length scales.
Assessed closed-loop integration of generative AI sequence design, structure prediction, high-throughput experimentation, and autonomous robotics.
Generative AI models resolve complex sequence-structure-function relationships in hierarchical protein building blocks, including silk, collagen, and amyloids.
Closed-loop platforms integrating predictive computational models with automated robotic validation substantially accelerate the discovery and optimization of programmable biomaterials with adaptive mechanics.