Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks-Reference-Cited by-同舟云学术

Improving Few- and Zero-Shot Reaction Template Prediction Using Modern Hopfield Networks

Published:2022-01-15 Issue:9 Volume:62 Page:2111-2120
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Seidl Philipp¹^ORCID,Renz Philipp¹^ORCID,Dyubankova Natalia²,Neves Paulo²,Verhoeven Jonas²,Wegner Jörg K.³^ORCID,Segler Marwin⁴^ORCID,Hochreiter Sepp¹⁵,Klambauer Günter¹

Affiliation:

1. ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Altenbergerstraße 69, Linz, Austria 4040

2. Janssen Pharmaceutica NV, High Dimensional Biology and Discovery Data Sciences, Janssen Research & Development, Turnhoutseweg 30, Beerse, Belgium 2340

3. Janssen Research & Development, LLC, In-Silico Discovery and External Innovation (ISD&EI), 1 Cambridge Center, 255 Main St, Cambridge, Massachusetts 02142, United States

4. Microsoft Research, 21 Station Road, Cambridge, United Kingdom CB1 2FB

5. Institute of Advanced Research in Artificial Intelligence, Landstraßer Hauptstraße 5, Wien, Austria 1030

Funder

Agentschap Innoveren en Ondernemen

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.1c01065

Reference87 articles.

1. The role of the medicinal chemist in drug discovery — then and now

2. Interpretable machine-learning strategy for soft-magnetic property and thermal stability in Fe-based metallic glasses

3. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

4. Computer-Aided Molecular Design

5. Challenges and opportunities in computer-aided molecular design

Cited by 39 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. Site-specific template generative approach for retrosynthetic planning;Nature Communications;2024-09-06

2. RLSynC: Offline–Online Reinforcement Learning for Synthon Completion;Journal of Chemical Information and Modeling;2024-08-18

3. READRetro: natural product biosynthesis predicting with retrieval‐augmented dual‐view retrosynthesis;New Phytologist;2024-07-30

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

5. Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions;Journal of Chemical Information and Modeling;2024-07-12