Can Pretrained Models Really Learn Better Molecular Representations for AI-Aided Drug Discovery?
Author:
Affiliation:
1. Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China
2. Tencent AI Lab, Shenzhen 518063, China
Funder
National Natural Science Foundation of China
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.3c01707
Reference31 articles.
1. Rethinking drug design in the artificial intelligence era
2. FraGAT: a fragment-oriented multi-scale graph attention model for molecular property prediction
3. TOP: A deep mixture representation learning method for boosting molecular toxicity prediction
4. Communicative Representation Learning on Attributed Molecular Graphs
5. Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism
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1. Editorial: Machine Learning in Bio-cheminformatics;Journal of Chemical Information and Modeling;2024-04-08
2. Bidirectional generation of structure and properties through a single molecular foundation model;Nature Communications;2024-03-14
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