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In recent years, self-supervised learning has emerged as a powerful tool to harness abundant unlabelled data for representation learning and has been broadly adopted in diverse areas.
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Do transformers really perform badly for graph representation?
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Mg-bert: leveraging unsupervised atomic representation learning for molecular property prediction
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Zinc20—a free ultralarge-scale chemical database for ligand discovery
John J Irwin, Khanh G Tang, Jennifer Young, Chinzorig Dandarchuluun, Benjamin R Wong, Munkhzul Khurelbaatar, Yurii S Moroz, John Mayfield, and Roger A Sayle · 2020
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Molecule attention transformer
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Self-supervised graph transformer on large-scale molecular data
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A graph to graphs framework for retrosynthesis prediction
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Correction to automated chemical reaction extraction from scientific literature
Jiang Guo, A Santiago Ibanez-Lopez, Hanyu Gao, Victor Quach, Connor W Coley, Klavs F Jensen, and Regina Barzilay · 2021
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Ogb-lsc: A large-scale challenge for machine learning on graphs
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Exposing the limitations of molecular machine learning with activity cliffs
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Chemical-reaction-aware molecule representation learning
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Improving machine learning performance on small chemical reaction data with unsupervised contrastive pretraining
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Can pre-trained models really learn better molecular representations for ai-aided drug discovery?
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Mole-bert: Rethinking pre-training graph neural networks for molecules
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Activity cliff prediction: Dataset and benchmark
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Uni-mol: A universal 3d molecular representation learning framework
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