Abstract—The advent of deep learning techniques has revolutionized the field of drug discovery, offering a promising approach for precision medicine in healthcare. This research paper aims to explore the transformative potential of deep learning in accelerating the identification and development of targeted therapies tailored to individual patient profiles. By leveraging large-scale biological and chemical data, deep learning algorithms have demonstrated remarkable capabilities in predicting molecular interactions, identifying drug candidates, and optimizing treatment regimens. This paper reviews the current state of deep learning-based drug discovery methods, highlighting their ability to uncover novel therapeutic targets, repurpose existing drugs, and facilitate the design of personalized treatment strategies. Furthermore, the ethical and regulatory considerations associated with the integration of deep learning in precision medicine are critically examined. Through a comprehensive analysis of the literature and case studies, this research paper elucidates the opportunities and challenges presented by deep learning-based drug discovery, emphasizing its potential to revolutionize the delivery of tailored healthcare interventions and improve patient outcomes.

Convolution neural network; Deep learning; Image classification,Drug Discovery,Precision Medicine,Health Care,Artificial Intelligence