This review article explores the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in the scientific revolution driven by extensive data, accessible toolkits, and robust computing resources. DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. The article provides a comprehensive overview of DL, featuring specific demonstrations of its notable applications in bioinformatics. It addresses challenges tailored for DL, highlights recent successes in structural bioinformatics, and presents a clear exposition of DL, from basic shallow neural networks to advanced models such as convolution, recurrent, artificial, and transformer neural networks. The paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics. Key points include the advantages of DL in handling large and complex data, automatic feature learning, improved performance, handling nonlinear relationships, and predictive modeling. However, it also addresses disadvantages such as lack of data, overfitting, data imbalance, interpretability, catastrophic forgetting, and high computational costs. The article concludes by highlighting the typical challenges with DL and offering solutions, aiming to provide insight into the future advancement and use of DL in bioinformatics.This review article explores the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in the scientific revolution driven by extensive data, accessible toolkits, and robust computing resources. DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. The article provides a comprehensive overview of DL, featuring specific demonstrations of its notable applications in bioinformatics. It addresses challenges tailored for DL, highlights recent successes in structural bioinformatics, and presents a clear exposition of DL, from basic shallow neural networks to advanced models such as convolution, recurrent, artificial, and transformer neural networks. The paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics. Key points include the advantages of DL in handling large and complex data, automatic feature learning, improved performance, handling nonlinear relationships, and predictive modeling. However, it also addresses disadvantages such as lack of data, overfitting, data imbalance, interpretability, catastrophic forgetting, and high computational costs. The article concludes by highlighting the typical challenges with DL and offering solutions, aiming to provide insight into the future advancement and use of DL in bioinformatics.