天美传媒

Abstract

Computational tools play a pivotal role in structure-based drug design (SBDD), significantly enhancing precision and efficiency in drug discovery. This article reviews the impact of key computational methods, including molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling, on optimizing drug-target interactions. These techniques enable accurate prediction of binding affinities, dynamic interactions, and biological activities, streamlining the drug development process. Additionally, the integration of artificial intelligence (AI) and machine learning (ML) has further advanced the field by automating analyses and generating novel drug candidates. Despite notable progress, challenges such as computational resource demands and model accuracy persist. Continued advancements in computational methods and technology promise to further revolutionize drug discovery, leading to more effective and targeted therapies.