Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy-Reference-Cited by-同舟云学术

Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy

Published:2020 Issue:12 Volume:11 Page:3316-3325
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Schwaller Philippe¹²³^ORCID,Petraglia Riccardo¹²³,Zullo Valerio⁴⁵⁶⁷,Nair Vishnu H.¹²³,Haeuselmann Rico Andreas¹²³,Pisoni Riccardo¹²³,Bekas Costas¹²³,Iuliano Anna⁴⁵⁶⁷^ORCID,Laino Teodoro¹²³

Affiliation:

1. IBM Research GmbH

2. Zurich

3. Switzerland

4. Department of Chemistry and Industrial Chemistry

5. University of Pisa

6. Pisa

7. Italy

Abstract

We present an extension of our Molecular Transformer model combined with a hyper-graph exploration strategy for automatic retrosynthesis route planning without human intervention.

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2020/SC/C9SC05704H

Reference64 articles.

1. Recent advances in the cross-coupling reactions of organoboron derivatives with organic electrophiles, 1995–1998

2. Zirconium/Nickel-Mediated One-Pot Ketone Synthesis

3. On Ni Catalysts for Catalytic, Asymmetric Ni/Cr-Mediated Coupling Reactions

4. The Logic of Chemical Synthesis: Multistep Synthesis of Complex Carbogenic Molecules(Nobel Lecture)

5. Computer‐Assisted Synthetic Planning: The End of the Beginning

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