This AI Reads Your Chemistry Instructions and Finds the Best Way to Build You a Molecule

In brief

• Synthegy, developed at EPFL, uses LLMs to rank synthesis routes against chemist-defined goals, matching expert judgments 71.2% of the time.
• The framework was validated against 36 independent chemists across 368 evaluations.
• The experiments reached alignment rates comparable to inter-expert agreement.

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.

Researchers led by Philippe Schwaller published a paper this week in Matter describing Synthegy, a framework that uses large language models as reasoning engines for chemical synthesis planning. The key insight is subtle but important: rather than asking AI to generate molecules, the team uses AI to evaluate synthesis routes that traditional software already produces.

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.

"When making tools for chemists, the user interface matters a lot, and previous tools relied on cumbersome filters and rules," said Andres M. Bran, lead author of the study, in a statement from EPFL.

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.