Traditional approaches to designing biochemical materials for biomedical use are often slow, labor-intensive, and constrained by the complexity of biological systems. This creates a critical gap between experimental material development and clinical translation. Artificial Intelligence (AI) now offers transformative solutions to this challenge by enabling predictive modeling, generative design, and rapid screening of novel biomaterials with tailored properties. As of 2025, AI is actively being used to design adaptive hydrogels, simulate protein-material interactions, and develop patient-specific drug delivery systems. Platforms like AlphaFold and generative neural networks are driving forward the rational design of bioactive materials, while AI-integrated high-throughput screening pipelines are drastically shortening the material discovery timeline. These innovations are accelerating healthcare solutions and bridging the translational gap.
