AI-driven material design for tissue engineering a comprehensive approach integrating generative adversarial networks and high-throughput experimentation

Abstract

This study presents an innovative AI-driven material design approach for tissue engineering, integrating generative adversarial networks (GANs) and high-throughput experimentation (HTE). The research methodology combines synthetic data generation, dimensionality reduction through principal component analysis (PCA), and model evaluation using a random forest classifier. The synthetic data, representative of diverse biomaterial structures, is generated with a three-class classification task. The model undergoes training on PCAtransformed and standardized synthetic data, with evaluation metrics including accuracy, precision, recall, and F1 score. Visualization through scatter plots, confusion matrices, and bar charts provides a comprehensive overview of the proposed approach’s efficacy. Results demonstrate the GAN’s capability to generate diverse synthetic data, the model’s focused learning during training, and its subsequent generalization in the testing phase. Mathematical functions, including sine and cosine, further illustrate fundamental principles, while performance metrics confirm the model’s proficiency in biomaterial classification. This research contributes to the evolving field of AI-driven material design, offering a systematic methodology and visual insights for accelerated and validated biomaterial discovery in tissue engineering applications.