The article discusses the growing application of deep learning (DL) in drug discovery, highlighting its potential to revolutionize various aspects of the field. DL, which has seen significant advancements in areas like image and voice recognition, natural language processing, and computer vision, is now being applied to drug discovery. The authors review the principles of DL, including the use of artificial neural networks (ANNs) with multiple layers of nonlinear processing units, and discuss specific architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected feed-forward networks. The article emphasizes the utility of DL in compound property and activity prediction, de novo molecular design, synthesis prediction, and biological image analysis. It highlights benchmark studies demonstrating the superior performance of DL models compared to traditional machine learning methods, particularly in multitask learning and multitasking scenarios. The authors also explore the use of graph convolution models and autoencoders for molecular representation learning, which can automatically generate molecular descriptors during training. In the context of de novo design, the article discusses the use of variational autoencoders (VAEs) and generative adversarial networks (GANs) to generate new chemical structures with desired properties. RNNs have also been applied to generate novel chemical structures and target-specific libraries, showing promising results. The article further examines the application of DL in predicting reactions and retrosynthetic analysis, where DL models have achieved performance comparable to or better than rule-based methods. CNNs have been used to score protein-ligand interactions, showing potential for improving scoring functions in molecular docking programs. Finally, the article reviews the application of DL in biological imaging analysis, including the segmentation and classification of microscopic images, tissue pathology, and imaging modalities such as CT, MRI, and PET. DL has demonstrated superior performance in these areas, enabling more accurate and efficient analysis of biological data. The authors conclude by discussing future developments in DL, including the use of memory-augmented neural networks and one-shot learning, and emphasize the need for large datasets and standardized evaluation platforms to further advance the field. They suggest that while DL is showing promise, it is still too early to draw firm conclusions about its superiority over other machine learning methods, and that the choice of method may depend on the specific problem and the modeler's familiarity with different techniques.The article discusses the growing application of deep learning (DL) in drug discovery, highlighting its potential to revolutionize various aspects of the field. DL, which has seen significant advancements in areas like image and voice recognition, natural language processing, and computer vision, is now being applied to drug discovery. The authors review the principles of DL, including the use of artificial neural networks (ANNs) with multiple layers of nonlinear processing units, and discuss specific architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and fully connected feed-forward networks. The article emphasizes the utility of DL in compound property and activity prediction, de novo molecular design, synthesis prediction, and biological image analysis. It highlights benchmark studies demonstrating the superior performance of DL models compared to traditional machine learning methods, particularly in multitask learning and multitasking scenarios. The authors also explore the use of graph convolution models and autoencoders for molecular representation learning, which can automatically generate molecular descriptors during training. In the context of de novo design, the article discusses the use of variational autoencoders (VAEs) and generative adversarial networks (GANs) to generate new chemical structures with desired properties. RNNs have also been applied to generate novel chemical structures and target-specific libraries, showing promising results. The article further examines the application of DL in predicting reactions and retrosynthetic analysis, where DL models have achieved performance comparable to or better than rule-based methods. CNNs have been used to score protein-ligand interactions, showing potential for improving scoring functions in molecular docking programs. Finally, the article reviews the application of DL in biological imaging analysis, including the segmentation and classification of microscopic images, tissue pathology, and imaging modalities such as CT, MRI, and PET. DL has demonstrated superior performance in these areas, enabling more accurate and efficient analysis of biological data. The authors conclude by discussing future developments in DL, including the use of memory-augmented neural networks and one-shot learning, and emphasize the need for large datasets and standardized evaluation platforms to further advance the field. They suggest that while DL is showing promise, it is still too early to draw firm conclusions about its superiority over other machine learning methods, and that the choice of method may depend on the specific problem and the modeler's familiarity with different techniques.