Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis

21 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Synthesis planning of new pharmaceutical compounds is a well-known bottleneck in modern drug design. Template-free methods, such as transformers, have recently been proposed as an alternative to template-based methods for single-step retrosynthetic predictions. Here, we trained and evaluated a transformer model, called Chemformer, for retrosynthesis predictions within drug discovery. The proprietary dataset used for training comprised ~18M reactions from literature, patents, and electronic lab notebooks. Chemformer was evaluated for the purpose of both single-step and multi-step retrosynthesis. We found that the single-step performance of Chemformer was especially good on reaction classes common in drug discovery, with most reaction classes showing a top-10 round-trip accuracy above 0.97. Moreover, Chemformer reached a higher round-trip accuracy compared to a template-based model. By analyzing multi-step retrosynthesis experiments, we observed that Chemformer found synthetic routes leading to commercial starting materials for 95% of the target compounds, an increase by more than 20% compared to the template-based model. In addition to this, we discovered that Chemformer suggested novel disconnections corresponding to reaction templates which are not included in the template-based model. The conclusions drawn from this work allow for designing a synthesis planning tool where template-based and template-free models work in harmony to optimize retrosynthetic recommendations.


Monte Carlo tree search
artificial intelligence
machine learning


Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.