Self-Driving Drug Discovery Integrated with Next-Gen Drug Delivery: AI–Robotics–Nanomedicine Closed Loops

Self-driving drug discovery is emerging as a transformative paradigm that combines artificial intelligence, automated synthesis, and advanced biological validation to accelerate the development of new therapeutics. Traditional pipelines often treat drug discovery and drug delivery as separate processes, leading to late-stage failures due to poor formulation, limited targeting, or inadequate clinical translation. In contrast, integrated closed-loop platforms connect AI-guided molecular design with robotic experimentation, organoid-based disease modeling, and adaptive nanomedicine optimization in a continuous feedback cycle. Reinforcement learning and generative models enable rapid exploration of chemical space, while automated laboratories provide real-time synthesis and screening of candidate compounds. Patient-derived organoids and organ-on-chip systems further improve predictive accuracy by capturing complex human-like responses. Simultaneously, next-generation drug delivery systems, including lipid nanoparticles, stimuli-responsive carriers, and biomimetic nanovesicles, can be optimized alongside drug candidates to enhance tissue specificity and therapeutic index. This review highlights recent advances in autonomous discovery–delivery integration, discusses key applications in drug-resistant infections, glioblastoma, and precision oncology, and outlines regulatory and translational challenges. Closed-loop self-driving frameworks may redefine future therapeutic development by enabling faster, more personalized, and delivery-aware drug innovation.