Accelerating green heterocyclic synthesis: Convergence of AI , automation, and sustainable chemistry

Abstract Heterocyclic compounds are among the most prevalent scaffolds in bioactive molecules, with approximately 82% of small‐molecule drugs approved by the US Food and Drug Administration (FDA) during 2013–2023 containing nitrogen heterocycles. Their synthesis, however, often relies on toxic solvents, stoichiometric reagents, and energy‐intensive conditions that generate substantial waste. Green chemistry strategies, including solvent‐free protocols, alternative reaction media, catalytic methods, and energy‐efficient activation, have reduced the environmental footprint of heterocyclic synthesis. However, optimal conditions are still commonly identified through trial‐and‐error experimentation. This review examines how the convergence of artificial intelligence (AI) and laboratory automation is addressing this bottleneck. We first survey established green synthesis strategies and their limitations. We then examine machine learning approaches for predictive reaction design and three major automation platforms—high‐throughput experimentation, continuous flow chemistry, and self‐driving laboratories—that translate these predictions into experimental practice. A central premise of this review is that incorporating quantitative green metrics, such as E‐factor and process mass intensity, as explicit objectives in AI‐driven optimization can make sustainability a prospective design criterion. Demonstrations now include heterocycle‐related pharmaceutical process development, although comparable validation across complete routes remains limited. We discuss the data infrastructure, scalability, and regulatory challenges that must be addressed to extend these results toward integrated autonomous green synthesis.