Generating diversity and securing completeness in algorithmic retrosynthesis

Abstract Chemical synthesis planning has considerably benefited from advances in the field of machine learning. Neural networks can reliably and accurately predict reactions leading to a given, possibly complex, molecule. In this work we focus on algorithms for assembling such predictions to a full...

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Bibliographic Details

Published in:	Journal of Cheminformatics
Main Authors:	Florian Mrugalla, Christopher Franz, Yannic Alber, Georg Mogk, Martín Villalba, Thomas Mrziglod, Kevin Schewior
Format:	Article
Language:	English
Published:	BMC 2025-05-01
Subjects:	Computer-Assisted Synthesis Planning (CASP) Retrosynthesis DFPN Chemical diversity score
Online Access:	https://doi.org/10.1186/s13321-025-00981-x