LLMs turn to atom-level editing to fix unsynthesizable generated molecules

Nature Machine Intelligence published SynCraft on 23 September: a framework that has large language models predict executable sequences of atom-level edits rather than generating SMILES strings directly, sidestepping the syntactic fragility of LLMs to push generated molecules over the "synthesis cliff".

The paper reports it outperforms state-of-the-art baselines in producing synthesizable analogues with high structural fidelity, and replicates expert medicinal-chemistry intuition by editing PLK1 inhibitors and rescuing discarded RIPK1 candidates. Note that these comparisons are the authors' own benchmarks; the full text is behind a paywall, so the specific deltas cannot be verified from the public abstract.

The code is MIT-licensed on GitHub, the test sets and training corpus (3,332 edit pairs with reasoning traces) are on Figshare, and the framework is packaged as an agent skill demonstrating an end-to-end rescue of SARS-CoV-2 main protease candidates. Whether drug-design teams actually adopt it is the next thing to watch.