Designing a molecule from scratch is one of chemistry's hardest problems. It's not just about knowing what atoms to connect—it's about knowing the right order of reactions, when to protect sensitive parts of the molecule, and how to avoid dead ends that could ruin months of lab work.
Traditionally, that knowledge lives in the heads of experienced chemists. Now, a team at EPFL wants to put it into a language model.
Here's how it works: A chemist types in a goal in plain English, something like "form the pyrimidine ring in the early stages." Existing retrosynthesis software—which works by breaking target molecules into simpler pieces—then generates dozens or hundreds of possible synthesis routes.
Synthegy converts each route into text and hands it to an LLM, which scores every route on how well it matches the chemist's instruction. The best ones float to the top, with written explanations of why.
The system was validated in a double-blind study involving 36 independent chemists who reviewed 368 route pairs. Their selections matched Synthegy's 71.2% of the time, a number that's roughly in line with how often expert chemists agree with each other. Senior researchers (professors and research scientists) agreed with Synthegy more often than PhD students, suggesting the system captures the same strategic intuitions that come with experience.
The framework also handles a second problem: reaction mechanism elucidation. This is the question of why a chemical reaction happens—what electron movements take place at each step. Synthegy breaks reactions into elementary moves and has the LLM assess each candidate step for chemical plausibility. On simple reactions like nucleophilic substitutions, the best models achieved near-perfect accuracy.
The paper acknowledges current limits. LLMs sometimes misread the direction of a reaction in its text representation, leading to wrong feasibility calls. Smaller models perform no better than random guessing. Routes longer than 20 steps are harder to track coherently.
