Graph transformer neural network for chemical reactivity prediction

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


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.


High-throughput experimentation
Late-stage functionalization
Geometric deep learning
Machine learning
Graph neural networks
Quantum chemistry
Drug discovery
Medicinal chemistry
Reaction prediction
C–H activation

Supplementary materials

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


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