Topic: AI-Driven Discovery and Design of Biomaterials: From Data to Biomedical Translation

Artificial intelligence (AI) and machine learning are rapidly transforming biomaterials research by enabling data-driven discovery, predictive modeling, inverse design, and accelerated experimental optimization. However, the major opportunity lies not simply in applying individual AI algorithms to biomaterials problems, but in establishing an integrated discovery pipeline that connects data acquisition and representation with predictive modeling, generative and inverse design, experimental validation, and ultimately biomedical translation.

This Special Topic will highlight emerging AI-driven approaches for the discovery, design, optimization, and biomedical evaluation of advanced biomaterials. Particular emphasis will be placed on the integration of materials data, biological information, computational modeling, and experimental workflows to establish efficient and reliable closed-loop discovery paradigms. Topics will span AI-enabled design of lipid, polymeric, hydrogel, and other biomaterial systems; generative models and inverse design; prediction of structure–property–biological response relationships; high-throughput experimentation and active learning; multimodal integration of materials data with multi-omics and biomedical imaging; and interpretable, generalizable, experimentally validated AI models.

By bringing together materials informatics, artificial intelligence, biomaterials science, and biomedical engineering, this Special Topic aims to establish a comprehensive framework for accelerating biomaterials discovery and translating computationally designed materials into practical biomedical applications.

Scope of the Special Topic

● AI- and machine learning-enabled design of lipid, polymeric, hydrogel, and other biomaterial systems;

● Generative models and inverse design of biomaterials;

● Prediction of structure–property–biological response relationships;

● High-throughput experimentation, active learning, and closed-loop discovery;

● Integration of materials data with multi-omics and biomedical imaging;

● Explainability, data quality, model generalizability, and experimental validation;

● Applications in drug delivery, tissue engineering, immunoengineering, and precision medicine.

 AI for biomaterials, generative AI, inverse design, materials informatics, closed-loop discovery