Artificial Intelligence and Machine Learning Approaches for Target-Based Drug Discovery: A Focus on GPCR-Ligand Interactions

Abstract

G protein-coupled receptors (GPCRs) represent one of the most significant classes of drug targets due to their pivotal roles in various physiological processes and disease mechanisms. Traditional methods of drug discovery targeting GPCR-ligand interactions are often time-consuming, resource-intensive, and limited by experimental constraints. The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized target-based drug discovery, offering innovative approaches to predict GPCR-ligand interactions with enhanced accuracy and efficiency. This review explores the integration of AI and ML techniques in GPCR-targeted drug discovery, highlighting their potential to accelerate lead identification, optimize ligand binding predictions, and improve structure-activity relationship modeling. We discuss various AI/ML algorithms, including supervised learning, deep learning, and reinforcement learning, and their applications in ligand-based and structure-based drug design. Additionally, we examine the challenges associated with data quality, model interpretability, and computational limitations. The review also emphasizes emerging trends, such as the use of neural networks and transfer learning, which are reshaping the landscape of drug discovery. By focusing on GPCR-ligand interactions, this paper provides insights into how AI and ML can transform traditional drug development processes, ultimately contributing to more effective and targeted therapeutics.