Advancements in computational biology have significant implications for drug discovery and various applications beyond protein engineering. These advancements enable more accurate predictions of protein structures and interactions, leading to improved understanding of disease mechanisms and potential drug targets. By leveraging deep learning models like SGNet, researchers can expedite drug discovery processes by efficiently screening compounds for binding affinity and specificity against target proteins. This accelerates drug development timelines and enhances precision medicine approaches tailored to individual patients' genetic profiles.