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Retrosynthetic Accessibility Score (RAscore) - Rapid Machine Learned Synthesizability Classification from AI Driven Retrosynthetic Planning

preprint
submitted on 29.09.2020 and posted on 30.09.2020 by Amol Thakkar, Veronika Chadimova, Esben Jannik Bjerrum, Ola Engkvist, Jean-Louis Reymond

Computer aided synthesis planning (CASP) is part of a suite of artificial intelligence (AI) based tools that are able to propose synthesis to a wide range of compounds. However, at present they are too slow to be used to screen the synthetic feasibility of millions of generated or enumerated compounds before identification of potential bioactivity by virtual screening (VS) workflows. Herein we report a machine learning (ML) based method capable of classifying whether a synthetic route can be identified for a particular compound or not by the CASP tool AiZynthFinder. The resulting ML models return a retrosynthetic accessibility score (RAscore) of any molecule of interest, and computes 4,500 times faster than retrosynthetic analysis performed by the underlying CASP tool. The RAscore should be useful for the pre-screening millions of virtual molecules from enumerated databases or generative models for synthetic accessibility and produce higher quality databases for virtual screening of biological activity.

Funding

SNF grant no. 200020_178998

Big Data in Chemistry

European Commission

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History

Email Address of Submitting Author

av.mol.th@gmail.com

Institution

University of Bern

Country

Switzerland

ORCID For Submitting Author

0000-0003-0403-4067

Declaration of Conflict of Interest

No conflict of interest

Version Notes

Version 1 - initial

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