Yingjun Wang1, *

1 National Engineering Research Center for Tissue Restoration and Reconstruction, South China
University of Technology, Guangzhou, 510641, China.

ABSTRACT: As a core component of medical devices, biomaterials are rapidly evolving towards intelligence, precision, personalization, multifunctionality, and biomimicry. However, their clinical application faces severe challenges, notably high rates of bacterial infection and the growing problem of antibiotic resistance. Existing conventional antibacterial materials often suffer from limited efficacy and significant cytotoxicity, failing to meet the basic clinical requirements for bio-adaptivity. Consequently, developing smart antibacterial materials that combine high antimicrobial efficiency with bio-adaptivity has become a key research focus. It is important to note that the performance of such materials heavily depends on the precise spatial orientation and structural control of active molecules, which conventional trial-anderror research methods struggle to achieve efficiently and controllably.To address these challenges, our research team adopted the Materials Genome Initiative as the core methodology, integrating high-throughput experimentation, high-throughput computation, and artificial intelligence to shift the research paradigm from traditional "experience-oriented" to a modern "data-driven" approach. Specifically, we developed a high-throughput experimental platform using gradient surfaces of active molecules, achieving an exponential increase in data
acquisition efficiency. Combined with high-throughput computational techniques, we introduced the concept of "bacteria-accessible surface area" for the first time internationally, revealing the intrinsic relationship between molecular orientation, surface charge, and antibacterial activity. Furthermore, by incorporating machine learning, we established biological performance regression models and grafting kinetic regression models, enabling the collaborative optimization of multiple material functions, including antibacterial action, osteogenesis promotion, vascularization, and neuroregeneration. Supported by the above theories and methods, the team successfully developed a series of bio-adaptive smart antibacterial materials, including fusion peptide-based, temperature-responsive, pH-responsive, and enzyme-responsive types. In the absence of infection, these materials exhibit no cytotoxicity and effectively promote tissue repair and integration. Under infectious conditions, they intelligently respond to environmental changes, rapidly activating a highly effective antibacterial mechanism with an antibacterial rate exceeding 99.99%. Currently, these materials are being applied in implants and devices across various fields such as orthopedics, dentistry, soft tissue repair, and cardiovascular medicine, and are progressively advancing into the stages of product development, clinical translation, and related standard establishment. Available in forms including solid implants, hydrogels, and sprays, they demonstrate broad clinical application prospects.In summary, the data-driven paradigm enabled by the Materials Genome Initiative provides crucial support for the intelligent design and precise fabrication of biomaterials. This study systematically demonstrates the successful application of this paradigm in developing bio-adaptive smart antibacterial materials, offering novel strategies and pathways to combat the global threat of drug-resistant bacteria and advance the next generation of biomaterials.
KEYWORDS: Materials Genome Initiative; antibacterial; high-throughput platform; molecular dynamics simulation; implant
REFERENCES:
[1] Z. Fang, M. Zhang, H. Wang, et al, Adv. Mater., 35(49), (2023) 2303253.
[2] Z. Fang, J. Chen, Y. Zhu, et al, Nat. Commun., 12(1), (2021) 3757.