Next-generation drug discovery: The AI revolution in pharmaceutical research

Abstract

The integration of artificial intelligence (AI) into the drug discovery pipeline is redefining pharmaceutical research by enhancing efficiency, predictive accuracy, and innovation. Traditional drug development, constrained by high costs, long timelines, and low success rates, is being transformed through deep learning, predictive modeling, and explainable AI (XAI). These tools accelerate target identification, lead optimization, and drug repurposing by enabling high-throughput interpretation of multi-omics datasets spanning genomics, proteomics, and metabolomics. Generative models, including variational autoencoders (VAEs), generative adversarial networks (GANs), and transformer-based architectures, enable the de novo design of bioactive compounds, while reinforcement learning refines molecular properties. Structure-based drug design has been advanced by graph neural networks (GNNs) and convolutional neural networks (CNNs), improving virtual screening and binding affinity prediction. The coupling of AI with quantum chemistry enhances molecular property estimation, reducing reliance on experimental validation. AI-driven prediction of drug–target interactions (DTIs) supports both repurposing efforts and pharmacovigilance. This review presents a polypharmacology-aware, feedback-to-discovery framework, in which translational signals, such as biomarkers, molecular subtypes, and pathway constraints, are reintegrated into target selection and compound optimization to enhance decision quality. Unlike previous reviews focused on isolated AI applications, it offers a unified, end-to-end synthesis spanning target discovery to regulatory translation. We distinguish foundation models that learn transferable molecular representations from generative models that synthesize new compounds. Together with multimodal learning, explainable AI, and closed-loop design–make–test–learn systems linking molecular design to automated synthesis, these advances outline a mechanism-informed roadmap for AI-driven discovery across the modern pharmaceutical pipeline.