Bioinformatics and AI in dental biomaterials: Applications in drug delivery for tissue engineering

Dental biomaterials play a vital role in restorative dentistry and tissue regeneration, but their design and clinical translation remain challenging due to complex material–tissue interactions, patient-specific variability, and lengthy trial-and-error development cycles. Artificial intelligence (AI) and bioinformatics have emerged as complementary approaches to address these challenges. This review highlights the synergistic roles of AI and bioinformatics in advancing dental biomaterials, focusing on two major application domains: drug delivery systems and tissue engineering. Key methodologies such as predictive modeling and biomaterial informatics are explored for accelerating the design of novel materials with optimized drug release profiles and scaffold architectures. In parallel, multi-omics data analysis and closed-loop feedback optimization are presented as powerful strategies for post-implementation analysis, enabling deep understanding of tissue responses and iterative refinement of biomaterial performance. By covering applications at both the design and post-implementation stages, this review underscores the value of integrating AI-driven models with bioinformatics-driven data insights in guiding advanced dental biomaterial development. The convergence of these technologies holds strong potential to enable smarter, personalized, and more efficient dental biomaterials, paving the way for next-generation patient-specific dental therapies. • Integrates AI/ML and bioinformatics to accelerate dental biomaterials across polymers, metals, ceramics, and nanomaterials. • Pre-design: predictive models optimize compositions, scaffolds, and drug-release kinetics. • Post-analysis: AI-driven imaging and multi-omics decoding quantify in vivo integration, efficacy, and safety. • Personalization: disease-specific omics guide payload selection; models tailor responsive carriers to patient profiles. • Roadmap: proposes a closed-loop, data-driven workflow and dataset standardization to speed clinical translation.