The fast and efficient synthesis of novel compounds continues to be a formidable challenge in small-molecule drug discovery, requiring considerable time and effort . The structural novelty and complexity of target molecules for synthesis often present substantial challenges, especially when establishing structure-activity relationships (SARs) in medicinal chemistry . Therefore, efficient synthetic strategies are crucial for advancing hit-to-lead and lead optimization programs, as they play a key role in enhancing or maintaining pharmacological activities while concomitantly improving the physicochemical properties of drug candidates . Advanced computer-assisted molecular design methods hold the potential to address these key aspects in medicinal chemistry . Leveraging computational tools and machine learning frameworks, combined with efficient semi-automated synthesis techniques such as high-throughput experimentation (HTE) and late-stage functionalization (LSF), improves cycle times while reducing material consumption and costs .