An overview of AI in biofunctional materials

The integration of artificial intelligence (AI) into biofunctional materials is transforming material design, synthesis, and optimization for medical applications. Machine learning and deep learning models now predict material properties (e.g., mechanical strength, degradation rate) with > 90% accuracy, dramatically reducing trial-and-error in scaffold and nanoparticle fabrication. AI-driven platforms accelerate surface functionalization strategies to enhance cell adhesion and drug loading, while generative models design stimuli-responsive hydrogels and smart polymers that mimic tissue mechanics. Case studies include rapid optimization of nanoparticle synthesis via Bayesian frameworks and the discovery of biodegradable stent materials through random forest screening. Despite remaining challenges in data quality and regulatory alignment, these advances underscore AI’s capacity to deliver high-performance, sustainable biomaterials and point toward an interdisciplinary roadmap for next-generation therapeutic solutions.

The review article "An Overview of AI in Biofunctional Materials" by Dazhou Li explores the transformative role of artificial intelligence (AI) in the design, synthesis, and optimization of biofunctional materials for medical applications. The integration of AI, particularly machine learning (ML) and deep learning (DL), has revolutionized traditional empirical approaches, enabling faster, more accurate, and cost-effective material discovery. Key highlights include AI's ability to predict material properties (e.g., mechanical strength, degradation rates) with over 90% accuracy, optimize synthesis conditions, and design stimuli-responsive hydrogels and smart polymers for tissue engineering and drug delivery.

The review begins by outlining the historical evolution of biomaterials, from primitive organic matter to advanced biodegradable polymers and bioactive ceramics. AI's predictive modeling and generative design capabilities, such as graph neural networks (GNNs) and generative adversarial networks (GANs), are highlighted as pivotal tools for inverse material design—creating materials based on desired properties rather than post-synthesis characterization. Case studies demonstrate AI's impact, such as the rapid optimization of poly(lactic-co-glycolic acid) (PLGA) nanoparticles and the discovery of biodegradable stent materials using random forest algorithms.

Challenges in the field are also addressed, including data heterogeneity, regulatory hurdles, and ethical concerns. Sparse and non-standardized datasets often limit model accuracy, necessitating frameworks like transfer learning to improve generalizability. Regulatory bodies like the FDA and EU are still developing guidelines for AI-aided materials, emphasizing the need for explainable AI (XAI) to ensure transparency and compliance. Ethical issues, such as algorithmic bias and data ownership, further complicate AI integration, underscoring the importance of interdisciplinary collaboration.

The review details methodologies like hybrid modeling, which combines physics-based simulations with data-driven surrogates, and high-throughput experimental techniques accelerated by AI. These approaches reduce development timelines from months to weeks, as seen in the optimization of PEG hydrogels using active learning. Market trends indicate robust growth in the biomaterials sector, driven by innovations in 3D printing, personalized medicine, and sustainable materials, with projections estimating the market to reach USD 386.98 billion by 2033.

Future directions emphasize advancements in AI-driven materials discovery, sustainability, and collaborative innovations. Autonomous laboratories, multi-modal data systems, and human-centric AI are expected to further streamline material development. The focus on biodegradable and bio-based materials aligns with global sustainability goals, while startups in nanofiber manufacturing and biomanufacturing are poised to commercialize breakthroughs.

In conclusion, AI holds immense potential to advance biofunctional materials, but challenges like data standardization, regulatory alignment, and ethical deployment must be addressed. The article calls for enhanced interdisciplinary collaboration, open-access datasets, and scalable infrastructure to unlock personalized, adaptive material solutions for healthcare and beyond. The integration of AI promises not only scientific progress but also improved patient outcomes and sustainable innovations in biomedicine.

This paper was published in Biofuctional Materials (ISSN: 2959-0582), an online multidisciplinary open access journal aiming to provide a peer-reviewed forum for innovation, research and development related to bioactive materials, biomedical materials, bio-inspired materials, bio-fabrications and other bio-functional materials.

The Article Processing Charges (APCs) are entirely waived for papers submitted before the end of 2025.
Citation: Li D. An overview of AI in biofunctional materials. Biofunct. Mater. 2025(2):0010, https://doi.org/10.55092/bm20250010.

DOI: 10.55092/bm20250010