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Molecular property prediction is essential in chemistry, especially for drug discovery applications.
Strategies for Pre-training Graph Neural Networks
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Molecule attention transformer
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Graph-aware transformer: Is attention all graphs need?
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Self-supervised graph transformer on large-scale molecular data, 2020
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Thawani, A. R., Griffiths, R.-R., Jamasb, A., Bourached, A., Jones, P., McCorkindale, W., Aldrick, A. A., and Lee, A. A · 2008
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How does multiple testing correction work? when prioritizing hits from a high-throughput experiment, it is important to correct for random events that falsely appear significant. how is this done and what methods should be used?
Noble, W. S · 2009
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ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G., and Ramsundar, B · 2010
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Molecular representation learning with language models and domain-relevant auxiliary tasks
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Extraction of chemical structures and reactions from the literature
Lowe, D. M · 2012
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Girshick, R. B., Donahue, J., Darrell, T., and Malik, J · 2013
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Semi-supervised classification with graph convolutional networks
Fine-tuned language models for text classification
Howard, J. and Ruder, S · 2018
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Randomized SMILES strings improve the quality of molecular generative models
Arús-Pous, J., Johansson, S. V., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J.-L., Chen, H., and Engkvist, O · 2019
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Molecular transformer: A model for uncertainty-calibrated chemical reaction prediction
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Hunter, C. A., Bekas, C., and Lee, A. A · 2019
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SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction
Wang, S., Guo, Y., Wang, Y., Sun, H., and Huang, J · 2019
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Pre-training molecular graph representation with 3D geometry
Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., and Tang, J · 2021
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Kipf, T. N. and Welling, M · 2016
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Order matters: Sequence to sequence for sets
Vinyals, O., Bengio, S., and Kudlur, M · 2016
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SMILES enumeration as data augmentation for neural network modeling of molecules
Bjerrum, E. J · 2017
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Schwaller, P., Gaudin, T., Lanyi, D., Bekas, C., and Laino, T · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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MoleculeNet: A benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V. S · 2017
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BERT: pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M., Lee, K., and Toutanova, K · 2018
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3D infomax improves gnns for molecular property prediction
Stärk, H., Beaini, D., Corso, G., Tossou, P., Dallago, C., Günnemann, S., and Liò, P · 2021
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Chemical-reaction-aware molecule representation learning
Wang, H., Li, W., Jin, X., Cho, K., Ji, H., Han, J., and Burke, M. D · 2021
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Do transformers really perform bad for graph representation?
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T · 2021
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Motif-based graph self-supervised learning for molecular property prediction, 2021
Zhang, Z., Liu, Q., Wang, H., Lu, C., and Lee, C.-K · 2021
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Pre-training molecular transformers through reaction prediction
Broberg, J · 2022
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Geometry-enhanced molecular representation learning for property prediction
Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., Wang, F., Wu, H., and Wang, H · 2022
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