Generative Artificial Intelligence for Advancing Discovery and Design in Biomateriomics

• Published in: Intelligent computing Vol. 4
• Main Authors: Pugliese, Raffaele, Badini, Silvia, Frontoni, Emanuele, Regondi, Stefano
• Format: Journal Article
• Language: English
• Published: American Association for the Advancement of Science (AAAS) 2025
• ISSN: 2771-5892; 2771-5892
• DOI: 10.34133/icomputing.0117

This review explores the transformative impact of generative artificial intelligence (AI) on the field of biomateriomics, an emerging interdisciplinary area that integrates materials science, biology, and engineering to study and design materials inspired by biological systems. We examine how generative AI techniques are revolutionizing the discovery, design, property prediction, and optimization of biomaterials across multiple scales and applications, particularly in tissue engineering, regenerative medicine, and drug discovery. Furthermore, we discuss the synergies between generative AI and other cutting-edge technologies, such as high-throughput 3-dimensional bioprinting, highlighting how these integrations are accelerating progress in the field. We also address the challenges and limitations of applying generative AI to biomateriomics, including issues related to data quality and availability, model interpretability, validation of AI-generated designs, and ethical considerations. Looking forward, future advancements, including multimodal AI systems and quantum–AI hybrids, promise to further expand the potential of biomateriomics, fostering innovation in sustainable materials, personalized medicine, and environmental applications. We hope that this comprehensive review, by providing insights into the current state of the field and future directions for innovation, will serve as a valuable resource for researchers, engineers, and policymakers working at the intersection of AI and biomateriomics.