Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction

11 May 2020, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Automated retrosynthetic planning algorithms are a research area of increased importance. Automated reaction template extraction from large datasets in conjunction with neural network enhanced tree search algorithms can find plausible routes to target compounds in seconds. However, the current way of training the neural networks to predict suitable templates for a given target product, leads to many predictions which are not applicable in silico. Most templates in the top-50 suggested templates can’t be applied to the target molecule to perform the virtual reaction. Here we describe how to generate data and train a neural network policy that predicts if templates are applicable or not. First, we generate a massive training dataset by applying each retrosynthetic template to each product from our reaction database. Second, we train a neural network to near perfect prediction of the applicability labels on a held-out test set. The trained network is then joined with a policy model trained to predict and prioritize templates using the labels from the original dataset. The combined model was found to outperform the policy model in a route-finding task using 1700 compounds from our internal drug discovery projects.


retrosynthesis prediction
Neural Network Prediction
tree search algorithm
reaction rules
data augmentation


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.