Can AI help design better biomedical materials?

Artificial intelligence (AI) is transforming the discovery and design of biomedical materials, particularly inorganic biomaterials like ceramics, nanozymes, and metal-organic frameworks, which are crucial for drug delivery, cancer therapy, anti-inflammatory treatments, and tissue engineering.
The review published in Science Bulletin describes how AI is accelerating material development by addressing complex biological properties through property prediction and inverse design.
Property prediction involves algorithms analyzing existing data to forecast material behavior in biological systems, while inverse design uses AI to propose material structures to achieve desired functions.
AI-assisted innovations in drug delivery, cancer treatment, inflammatory diseases, and tissue engineering highlight several applications where AI is making a significant impact.
Generative AI models and large-scale computational tools like graph neural networks are expanding the potential space for discovering entirely new material structures.
While challenges remain, including data quality, model interpretability, and the need for extensive experimental validation, AI holds the promise to make biomaterial discovery more efficient and autonomous.
The goal is to develop an AI-driven pipeline for materials discovery that closely connects clinical needs with material design, paving the way for safer and more effective therapies.