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‘Ring Breaker’: Neural Network Driven Synthesis Prediction of the Ring System Chemical Space

revised on 20.04.2020, 13:20 and posted on 21.04.2020, 12:25 by Amol Thakkar, Nidhal Selmi, Jean-Louis Reymond, Ola Engkvist, Esben Jannik Bjerrum

Ring systems in pharmaceuticals, agrochemicals and dyes are ubiquitous chemical motifs. Whilst the synthesis of common ring systems is well described, and novel ring systems can be readily computationally enumerated, the synthetic accessibility of unprecedented ring systems remains a challenge. ‘Ring Breaker’ uses a data-driven approach to enable the prediction of ring-forming reactions, for which we have demonstrated its utility on frequently found and unprecedented ring systems, in agreement with literature syntheses. We demonstrate the performance of the neural network on a range of ring fragments from the ZINC and DrugBank databases and highlight its potential for incorporation into computer aided synthesis planning tools. These approaches to ring formation and retrosynthetic disconnection offer opportunities for chemists to explore and select more efficient syntheses/synthetic routes.


Amol Thakkar is supported financially by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement no. 676434, “Big Data in Chemistry” (“BIGCHEM,”


Email Address of Submitting Author


University of Bern/AstraZeneca



ORCID For Submitting Author


Declaration of Conflict of Interest

No Conflict of Interest

Version Notes

Version (3) - Revisions, Manuscript under review