Deep learning has made significant progress in drug discovery, offering new tools for bioactivity prediction, molecular design, and biological image analysis. The field has evolved from traditional machine learning methods like support vector machines (SVM), random forests (RF), and neural networks (NN) to more advanced techniques such as convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoders (AE). These models leverage large datasets and powerful computing resources to improve accuracy and efficiency in drug discovery tasks. Deep learning models, such as deep neural networks (DNN), have been successfully applied to predict compound activity and properties. For example, DNNs have outperformed traditional methods in tasks like predicting BACE activity and drug-induced liver injury. Multitask DNNs have also shown superior performance in predicting multiple targets simultaneously. Additionally, deep learning has been used to generate new chemical structures through variational autoencoders (VAE) and generative adversarial networks (GAN), enabling the design of molecules with desired properties. In reaction prediction and retrosynthetic analysis, deep learning models have been used to predict reaction outcomes and design synthetic pathways. These models have shown promising results, often outperforming traditional rule-based methods. CNNs have also been applied to predict ligand-protein interactions, with models outperforming existing scoring functions in some cases. In biological image analysis, deep learning has enabled the segmentation and classification of cells, tissues, and subcellular structures. CNNs have been used for tasks such as cell tracking, colony counting, and histopathological diagnosis. These models have demonstrated superior performance compared to classical classifiers. Despite these advancements, challenges remain, including the need for large datasets and the potential for mode collapse in generative models. However, deep learning continues to evolve, with new architectures and techniques being developed to address these issues. The integration of deep learning into drug discovery is expected to accelerate the development of new drugs and improve the efficiency of the discovery process.Deep learning has made significant progress in drug discovery, offering new tools for bioactivity prediction, molecular design, and biological image analysis. The field has evolved from traditional machine learning methods like support vector machines (SVM), random forests (RF), and neural networks (NN) to more advanced techniques such as convolutional neural networks (CNN), recurrent neural networks (RNN), and autoencoders (AE). These models leverage large datasets and powerful computing resources to improve accuracy and efficiency in drug discovery tasks. Deep learning models, such as deep neural networks (DNN), have been successfully applied to predict compound activity and properties. For example, DNNs have outperformed traditional methods in tasks like predicting BACE activity and drug-induced liver injury. Multitask DNNs have also shown superior performance in predicting multiple targets simultaneously. Additionally, deep learning has been used to generate new chemical structures through variational autoencoders (VAE) and generative adversarial networks (GAN), enabling the design of molecules with desired properties. In reaction prediction and retrosynthetic analysis, deep learning models have been used to predict reaction outcomes and design synthetic pathways. These models have shown promising results, often outperforming traditional rule-based methods. CNNs have also been applied to predict ligand-protein interactions, with models outperforming existing scoring functions in some cases. In biological image analysis, deep learning has enabled the segmentation and classification of cells, tissues, and subcellular structures. CNNs have been used for tasks such as cell tracking, colony counting, and histopathological diagnosis. These models have demonstrated superior performance compared to classical classifiers. Despite these advancements, challenges remain, including the need for large datasets and the potential for mode collapse in generative models. However, deep learning continues to evolve, with new architectures and techniques being developed to address these issues. The integration of deep learning into drug discovery is expected to accelerate the development of new drugs and improve the efficiency of the discovery process.