Over the last few years, artificial intelligence (AI) has emerged as a robust force in biomedical research, and especially in drug development—an inherently complex but fundamentally important area, where efficiency and effectiveness have been major barriers in the past. With the transformational capabilities in molecular pharmacology, AI is helping revolutionize research by offering molecular pharmacology on virtual screening of chemical libraries, de novo drug design, molecular interactions, prediction of pharmacokinetic properties, prediction of drug target interactions, prediction of toxicities, patient stratification, clinical trial optimization, improved molecular structures, and potential side effects. These algorithms can help cascade the identification of lead compounds and optimization of the therapeutic candidate through the drug development process. Using traditional methods, it is difficult to identify the complex interplay of molecules and pathways in diseases. Making drug development more efficient and cost-effective, traditional pharmacological approaches are transforming to the use of AI algorithms for analyzing massive biological datasets and modeling complex biological systems like genomics and proteomics to identify disease-associated targets and predict interaction with potential drug candidates using network analysis tools and graph neural networks. Gene expression changes in diseased cells can be analyzed using AI in identifying novel drug targets and identifying the critical pathways for intervention.