Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning-Reference-Cited by-同舟云学术

Quantum chemical data generation as fill-in for reliability enhancement of machine-learning reaction and retrosynthesis planning

Published:2023 Issue:3 Volume:2 Page:663-673
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Toniato Alessandra¹²³⁴^ORCID,Unsleber Jan P.¹²^ORCID,Vaucher Alain C.³⁴^ORCID,Weymuth Thomas¹²^ORCID,Probst Daniel³⁴^ORCID,Laino Teodoro³⁴^ORCID,Reiher Markus¹²^ORCID

Affiliation:

1. Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, 8093 Zurich, Switzerland

2. National Center for Competence in Research-Catalysis (NCCR Catalysis), ETH Zurich, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland

3. IBM Research Europe, 8803 Rüschlikon, Switzerland

4. National Center for Competence in Research-Catalysis (NCCR Catalysis), IBM Research, 8803 Rüschlikon, Switzerland

Abstract

We demonstrate and discuss the feasibility of autonomous first-principles mechanistic explorations for providing quantum chemical data to enhance the confidence of data-driven retrosynthetic and synthesis design based on molecular transformers.

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00006K

Reference90 articles.

1. Molecular property prediction: recent trends in the era of artificial intelligence

2. P.Schwaller , A. C.Vaucher , T.Laino and J.-L.Reymond , ChemRxiv , 2020 , preprint, 10.26434/chemrxiv.12758474.v2

3. Deep learning for molecular design—a review of the state of the art

4. De novo molecular design and generative models

5. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review;ACS Synthetic Biology;2024-07-24

2. TS‐tools: Rapid and automated localization of transition states based on a textual reaction SMILES input;Journal of Computational Chemistry;2024-06-08

3. Machine learning advancements in organic synthesis: A focused exploration of artificial intelligence applications in chemistry;Artificial Intelligence Chemistry;2024-06

4. A human-machine interface for automatic exploration of chemical reaction networks;Nature Communications;2024-05-01

5. Accelerating Reaction Network Explorations with Automated Reaction Template Extraction and Application;Journal of Chemical Information and Modeling;2023-05-22