Graph transformer neural network for chemical reactivity prediction

16 May 2023, Version 1


Optimizing the properties of advanced drug candidates can be facilitated by directly introducing certain chemical groups without having to synthesize the molecules from scratch. However, their chemical complexity often renders reactivity predictions and synthesis planning challenging. Herein, we introduce a graph transformer neural network (GTNN) approach for computational reaction screening and identification of substrates suitable for late-stage functionalization, taking compound alkylation via Minisci-type chemistry as an example. GTNNs were trained on experimentally generated reactions obtained from miniaturized high-throughput experimentation and literature data. Trained models were prospectively applied to predicting the reactivity of 3180 advanced heterocyclic molecules, identifying potential substrates for Minisci-type alkylation. All predicted substrates were experimentally confirmed. Multiple chemical transformations were identified for each of these compounds. Selected hits were scaled up, isolated, and characterized, delivering 30 novel, suitably functionalized molecules for medicinal chemistry. These results positively advocate GTNN models for reactivity prediction in drug discovery.


Supplementary materials

Supplementary Information: Graph transformer neural networks for chemical reactivity prediction
Supplementary information to the main manuscript.


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.