Guiding large language models to predict edit sequences for molecular synthesizability optimization

TL;DR - Li and Lai use large language models to predict structural edit sequences that make computationally designed molecules more synthesizable. The approach reportedly outperforms traditional methods while preserving important molecular features.

• Targets a key drug-design failure mode: generated molecules that cannot be practically synthesized.
• Predicts precise, sequential modifications to molecular structures rather than merely scoring synthesizability.
• Aims to improve synthesizability without sacrificing key properties of the original candidate.
• The provided summary reports better performance than traditional optimization methods but does not include quantitative results.