AI-Driven Design and Biocompatibility Assessment of Functional Biomaterials

Dear Colleagues,

The field of functional biomaterials is undergoing a paradigm shift, moving from traditional, often unexpected discovery to a rational, data-driven design approach. This Special Issue, titled "AI-Driven Design and Biocompatibility Assessment of Functional Biomaterials," will capture the cutting edge of this transformation by highlighting the integral roles of Artificial Intelligence (AI) and Machine Learning (ML).

The traditional development of biomaterials is a time-consuming and costly process, particularly when ensuring complex biological responses such as biocompatibility, biointegration, and immunomodulation. AI and ML offer powerful tools to overcome these challenges. They can decipher complex structure–property–performance relationships from large datasets, predict new material formulations with tailored functionalities, and model biological responses in silico before costly laboratory experiments and clinical trials.

This Special Issue will serve as a platform for original research and review articles that explore the synergy between computational intelligence and biomaterials science. We welcome contributions that demonstrate the application of ML, deep learning, and other AI methodologies in the following areas:

• Predictive Design: Using AI for the de novo design and virtual screening of polymers, alloys, ceramics, and hydrogels with specific mechanical, chemical, and biological properties;
• Biocompatibility and Toxicity Prediction: Developing ML models to forecast cellular responses, inflammatory potential, hemocompatibility, and long-term in vivo performance of new materials;
• Optimization of Manufacturing: Applying AI algorithms to optimize processing parameters (e.g., 3D printing, electrospinning, microfluidics) for enhanced material performance and consistency;
• Multi-Omics Integration: Leveraging AI to correlate biomaterial properties with high-throughput genomic, proteomic, and metabolomic data to unravel underlying mechanisms of biointeraction;
• Accelerated Regulatory Science: Exploring the role of AI-generated models and digital twins in supporting the safety and efficacy evaluation of biomaterials for regulatory submissions.

By bringing together interdisciplinary research at the intersection of materials science, computer science, and biology, this Special Issue will accelerate the development of the next generation of "intelligent" functional biomaterials, paving the way for more advanced, safe, and personalized medical devices, implants, and drug delivery systems.

Dr. Ioannis Liakos
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Functional Biomaterials is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.