Authors: Hesam Mirmohammadi | Abdalbseet A. Fatalla | Ayesha Razi | Pedram Iranmanesh | Amirsalar Khandan | Reza Barbaz‐Isfahani | Mohammad Sabeti | Jafar Kolahi | P. M. H. Dummer
Source: Dental Hypotheses (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Enhancing the Role of AI in the Development of Biomaterials through Interdisciplinary Collaboration The age of artificial intelligence (AI) opens up new perspectives across science and technology. Among the most promising perspectives is the integration of AI into the discovery, design, and optimization of biomaterials. Predictive modeling of biomaterial properties using AI is transforming biomaterial research by rapid data-based predictions of important characteristics such as mechanical strength, biocompatibility, and degradation rates. Traditionally, the development of a new biomaterial has followed a hypothesis-based trial-and-error methodology. While this has yielded success, it is time-consuming and resources-intensive. Nowadays, machine learning models can predict the properties of biomaterials, guide their synthesis, and optimize existing materials. Machine learning models can link molecular descriptors, e.g., chemical compositions and amino acid sequences to macroscale properties like biocompatibility. Regression and classification algorithms can identify patterns in large datasets to predict how a new material will perform, reducing reliance on traditional trial-and-error experimentation.[1] AI algorithms can also analyze chemical structures to estimate immune responses, reducing the need for in vivo testing.[2] In addition, AI can rapidly screen thousands of material combinations. Nonlinear models can uncover hidden relationships between molecular features and bulk properties. Yet, their effectiveness of these models hinges on the quality, diversity, and depth of data they are trained on, as well as the contextual understanding of biological systems. This is where interdisciplinary collaboration becomes essential. Biomaterial research is an inherently interdisciplinary field. Biomaterial scientists provide expertise in synthesis and structural analyses; biologists continue insights into cellular behavior and immune system reactions; finally, clinical researchers contribute to patient-centered outcomes. In some cases, AI scientists optimize the AI algorithms themselves; however, without close collaboration with other research fields, AI models can become overfitted, misinterpreted, trained on inadequate and narrow data, and detached from a clinically relevant context. By working together, interdisciplinary teams can generate richer datasets and ensure that AI-driven predications translate into meaningful experimental designs and clinical applications. Collaboration also plays a critical role in addressing the limitations of AI algorithmic bias, data scarcity, and the black-box nature of some machine-learning models, all of which pose challenges to adaptations.[3] The integration of AI into biomaterial sciences should not be viewed as a replacement for established current methods, but rather as a complementary approach that magnifies human expertise. Real innovation will not come from AI in isolation but from the synergy of computational power with the creativity and intuition of humans across diverse scientific disciplines. As funding agencies, research institutions, and academic journals shape the future of research, they should actively support platforms that bring together data scientists, material engineers, clinical scientists, and regulatory experts. As an illustrative example, consider the development of a peptide with AI to inhibit the Sortase A enzyme in Streptococcus mutants.[4] This enzyme plays a critical role in anchoring surface proteins that mediate adhesion and biofilm formation. Machine learning models can predict and generate peptide sequences tailored to the active sites of Sortase A to inhibit this enzyme. In silico molecular docking simulations and molecular dynamics refinements allow rapid screening of candidate peptides. Multi-objective optimization evaluates stability in acidic oral environments and specificity for pathogenic strains. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest. Biomaterial ; Support vector machine ; Computational model Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Biomedical and Engineering Education