Deep learning in drug discovery: applications and limitations

Abstract

Drug discovery is a complex and challenging process that requires a significant amount of time and resources. The application of deep learning in drug discovery has the potential to revolutionize the field by offering more efficient and accurate methods for predicting drug-target interactions, designing new drugs, and predicting toxicity and side effects. However, there are also several limitations and challenges associated with the use of deep learning in drug discovery, including the lack of high-quality training data, overfitting and generalization issues, interpretability and explainability of deep learning models, and legal and ethical considerations. In this review article, we discuss the various applications of deep learning in drug discovery, provide examples of successful applications, and explore the potential benefits of using deep learning. We also discuss the limitations and challenges associated with the use of deep learning and suggest ways in which these challenges can be addressed. Furthermore, we discuss the future directions of research in this area, identify areas where more research is needed, and provide recommendations for future research. Overall, this review article highlights the potential of deep learning in drug discovery and provides insights into the challenges and opportunities associated with its use.