AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery

Artificial intelligence (AI) is transforming synthetic chemistry from task-specific predictors into integrated platforms that unify retrosynthesis, reaction optimization, and closed-loop robotic automation. This review highlights how AI-assisted planning and robotic execution shorten cycle times, reduce step counts, and improve route sustainability in medicinal chemistry. Recent advances, including large language models (LLMs), template-free retrosynthesis, and Bayesian optimization, are evaluated alongside key limitations in dataset quality, reproducibility, and deployment costs. To ensure translational relevance, reproducible benchmarks such as step count, time-to-in vitro, and green metrics (E-factor, process mass intensity) are emphasized. This review proposes a hierarchical framework structured across three interconnected levels: cognitive planning, physical execution, and translational evaluation. Within this structure, key elements include LLM-based synthesis planning, robotic and closed-loop execution, interpretable decision-making, sustainability-by-design, advanced reaction optimization, and multi-objective retrosynthesis. Together, these components provide a conceptual basis for integrating digital intelligence with physical experimentation. By embedding green chemistry principles and regulatory awareness, AI is increasingly positioned not only as a predictive tool but also as an assistive collaborator supporting decision-making in medicinal chemistry workflows. The convergence of AI, robotics, and sustainability metrics highlights an emerging transition; however, realizing a future where every experiment reliably feeds back into autonomous learning loops requires overcoming significant current barriers in data standardization and hardware interoperability.