Application of Artificial Neural Network Algorithm in Optimizing Biomaterial Design

The integration of nanobiotechnology has significantly improved their functionality. However, previous design methods relied on experience and trial and error, resulting in low efficiency. This article aims to discuss the powerful learning and optimization capabilities of artificial neural networks, which take the composition, structure, performance and other parameters of biomaterials as inputs, and through network learning and training, achieve accurate analysis and prediction. However, previous design methods relied on experience and trial and error, resulting in low efficiency. This article aims to discuss the powerful learning and optimization capabilities of artificial neural networks, which take the composition, structure, performance and other parameters of biomaterials as inputs, and through network learning and training, achieve accurate analysis and prediction. Therefore, this article used experimental and comparative methods to construct a model for predicting and training natural silk and regenerated silk protein materials. The experimental results show that the prediction accuracy is over 92%, and the recall rate is over 87%.