Chemist feedback teaches AI to rank synthesis routes more sensibly
- A route-ranking network is first pretrained on a large body of routes taken from the literature, then refined with a smaller set of chemist-labelled feasibility judgments.
- Held-out targets and an independent multi-chemist dataset were used for testing.
- Top-ranked routes were mostly judged workable, including many that differed from the published route.
- Expert fine-tuning brought the model's preferences noticeably closer to chemists' consensus, approaching the level at which individual experts agree with their peers.
💡 Points Based on the abstract
Agreement with a chemist panel is a softer target than it sounds: route feasibility is subjective, and a model can score well while still favoring routes that look tidy on paper. What I would test on our own series is whether it flags the steps we know fail, such as late-stage selectivity problems or awkward protecting-group manipulations, and whether it still holds up on scaffolds unlike its training set.
📄 Paper · Journal of Chemical Information and Modeling (ACS) ↗ Expert-Augmented Deep Learning Approach for Synthesis Route Evaluation