AI-driven hypergraph network of organic chemistry: network statistics and applications in reaction classification-Reference-Cited by-同舟云学术

AI-driven hypergraph network of organic chemistry: network statistics and applications in reaction classification

Published:2023 Issue:3 Volume:8 Page:619-635
ISSN:2058-9883
Container-title:Reaction Chemistry & Engineering
language:en
Short-container-title:React. Chem. Eng.

Author:

Mann Vipul¹^ORCID,Venkatasubramanian Venkat¹^ORCID

Affiliation:

1. Department of Chemical Engineering, Columbia University, New York, USA

Abstract

Generated a hypergraph-based network of organic chemistry where hyperedges represent reactions and nodes represent reactants and products. The statistics of such hypergraph network is reported along with AI applications for predicting reaction class.

Funder

Division of Emerging Frontiers in Research and Innovation

Publisher

Royal Society of Chemistry (RSC)

Subject

Fluid Flow and Transfer Processes,Process Chemistry and Technology,Chemical Engineering (miscellaneous),Chemistry (miscellaneous),Catalysis

Link

http://pubs.rsc.org/en/content/articlepdf/2023/RE/D2RE00309K

Reference31 articles.

1. Artificial intelligence in reaction prediction and chemical synthesis

2. Hybrid, Interpretable Machine Learning for Thermodynamic Property Estimation using Grammar2vec for Molecular Representation

3. Predicting chemical reaction outcomes: A grammar ontology‐based transformer framework

4. Retrosynthesis prediction using grammar-based neural machine translation: An information-theoretic approach

Cited by 7 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. Learning spatial–temporal pairwise and high-order relationships for short-term passenger flow prediction in urban rail transit;Expert Systems with Applications;2024-07

2. eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning;Computers & Chemical Engineering;2024-02

3. Computer Vision Algorithm for Characterization of a Turbulent Gas–Liquid Jet;Inventions;2024-01-04

4. SUSIE: Pharmaceutical CMC ontology-based information extraction for drug development using machine learning;Computers & Chemical Engineering;2023-11

5. G‐MATT: Single‐step retrosynthesis prediction using molecular grammar tree transformer;AIChE Journal;2023-10-12