Evolving biomaterials design from trial and error to intelligent innovation.
Journal:
Acta biomaterialia
PMID:
40081552
Abstract
The design and exploration of biomaterials plays a pivotal role in many fields, including medical and engineering. The prevailing approach to biomaterials discovery relies on orthogonal experiments, with repeated attempts to optimize experimental conditions. This method has proven invaluable in gaining experience, but it is also inefficient and challenging to predict the behavior of complex systems. The advent of high-throughput screening (HTS) techniques has led to a notable enhancement in the efficiency of biomaterials development, enabling researchers to assess a vast array of material combinations within a relatively short timeframe. Nevertheless, the emergence of artificial intelligence (AI) has been the catalyst for a new era in biomaterials design. AI has markedly accelerated the development of new materials by enabling the prediction of material properties through machine learning (ML) and deep learning models, as well as optimizing the design pipeline. This review will present a systematic overview of the development of biomaterials design technology. It will also explore the integration of AI with HTS technology and envisage the potential of AI-driven materials design in biomaterials for the future. STATEMENT OF SIGNIFICANCE: The design and synthesis of biomaterials have undergone substantial shifts, reflecting evolving research paradigms. High-throughput screening has emerged as a broad and efficient alternative to traditional free-form combination methods in biomaterial design. The advent of artificial intelligence (AI) enables personalized biomaterial design and, as a transformative tool in biomaterial development, is poised to redefine the field and offer long-term solutions for its advancement. Building on these advancements, this review systematically summarizes the evolution of biomaterial design, offering insights into the future trajectory of the field.