Deep learning (DL) has significantly transformed structural bioinformatics, enabling advancements in protein structure prediction, drug discovery, and molecular analysis. This review explores DL's role in this field, highlighting its applications, challenges, and future potential. DL, a subset of machine learning, uses artificial neural networks (ANNs) with multiple layers to process complex data. It has been successfully applied in tasks such as protein structure prediction, where models like AlphaFold have achieved remarkable accuracy by learning from protein sequences. DL also aids in predicting protein interactions, secondary structures, and post-translational modifications, improving the efficiency and accuracy of bioinformatics research. DL excels in handling large, complex datasets and nonlinear relationships, making it suitable for tasks like image analysis, sequence prediction, and molecular modeling. It can automatically learn features from data, reducing the need for manual feature engineering. DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have been applied in various structural bioinformatics tasks, including protein structure prediction, drug discovery, and molecular docking. For example, CNNs are used for image-based tasks, while RNNs and transformers are effective for sequence-based predictions. Despite its advantages, DL faces challenges such as data scarcity, overfitting, and computational demands. Techniques like model compression and efficient training strategies are being developed to address these issues. Additionally, interpretability and the ability to handle missing data remain important considerations in DL applications. In conclusion, DL is revolutionizing structural bioinformatics by enabling more accurate and efficient predictions of protein structures and functions. As data and computational resources continue to grow, DL is expected to play an even greater role in advancing biological research and drug discovery.Deep learning (DL) has significantly transformed structural bioinformatics, enabling advancements in protein structure prediction, drug discovery, and molecular analysis. This review explores DL's role in this field, highlighting its applications, challenges, and future potential. DL, a subset of machine learning, uses artificial neural networks (ANNs) with multiple layers to process complex data. It has been successfully applied in tasks such as protein structure prediction, where models like AlphaFold have achieved remarkable accuracy by learning from protein sequences. DL also aids in predicting protein interactions, secondary structures, and post-translational modifications, improving the efficiency and accuracy of bioinformatics research. DL excels in handling large, complex datasets and nonlinear relationships, making it suitable for tasks like image analysis, sequence prediction, and molecular modeling. It can automatically learn features from data, reducing the need for manual feature engineering. DL models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have been applied in various structural bioinformatics tasks, including protein structure prediction, drug discovery, and molecular docking. For example, CNNs are used for image-based tasks, while RNNs and transformers are effective for sequence-based predictions. Despite its advantages, DL faces challenges such as data scarcity, overfitting, and computational demands. Techniques like model compression and efficient training strategies are being developed to address these issues. Additionally, interpretability and the ability to handle missing data remain important considerations in DL applications. In conclusion, DL is revolutionizing structural bioinformatics by enabling more accurate and efficient predictions of protein structures and functions. As data and computational resources continue to grow, DL is expected to play an even greater role in advancing biological research and drug discovery.