The elephant in the lab: synthesizability in generative small-molecule design

The design of small molecules with tailored properties is a central goal in chemistry and materials science. Recent advances in machine learning provide powerful tools to accelerate the pace of discovery. One promising avenue for acceleration involves the use of generative models that propose novel candidates for diverse optimization tasks. Despite their promise, these methods are often evaluated solely using computational benchmarks, and many studies fail to advance proposed candidates to experimental validation in the wet lab. A key reason for this gap, the elephant in the room, is the limited synthesizability of the generated molecules. In response, the community has recently developed various strategies to address this challenge and incorporate synthesizability into generative design workflows. In this opinion, we provide a comprehensive overview of recent contributions that explicitly tackle molecular synthesizability, highlighting notable advances. We also discuss key limitations of current approaches and outline promising directions for future research.