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Procuring expressive molecular representations underpins AI-driven molecule design and scientific discovery.
Path-augmented graph transformer network
Chen, B.; Barzilay, R.; and Jaakkola, T. 2019 · 1905
Earlier work this paper cites.
Cormorant: Covariant molecular neural networks
Anderson, B.; Hy, T.-S.; and Kondor, R. 2019 · 1906
Earlier work this paper cites.
Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Honda, S.; Shi, S.; and Ueda, H. R. 2019 · 1911
Earlier work this paper cites.
Computational modeling of β \beta -secretase 1 (BACE-1) inhibitors using ligand based approaches
Subramanian, G.; Ramsundar, B.; Pande, V.; and Denny, R. A. 2016 · 1949
Earlier work this paper cites.
Molecule attention transformer
Maziarka, Ł.; Danel, T.; Mucha, S.; Rataj, K.; Tabor, J.; and Jastrzebski, S. 2020 · 2002
Earlier work this paper cites.
Directional message passing for molecular graphs
Klicpera, J.; Groß, J.; and Günnemann, S. 2020 · 2003
Earlier work this paper cites.
FLAT: Chinese NER using flat-lattice transformer
Li, X.; Yan, H.; Qiu, X.; and Huang, X. 2020 · 2004
Earlier work this paper cites.
Lite transformer with long-short range attention
Wu, Z.; Liu, Z.; Lin, J.; Lin, Y.; and Han, S. 2020 · 2004
Earlier work this paper cites.
The PDBbind database: methodologies and updates
Wang, R.; Fang, X.; Lu, Y.; Yang, C.-Y.; and Wang, S. 2005 · 2005
Earlier work this paper cites.
Se (3)-transformers: 3d roto-translation equivariant attention networks
Fuchs, F. B.; Worrall, D. E.; Fischer, V.; and Welling, M. 2020 · 2006
Earlier work this paper cites.
Efficient detection of network motifs
Wernicke, S. 2006 · 2006
Earlier work this paper cites.
Elnaggar, A.; Heinzinger, M.; Dallago, C.; Rihawi, G.; Wang, Y.; Jones, L.; Gibbs, T.; Feher, T.; Angerer, C.; Steinegger, M.; et al. 2020 · 2007
Earlier work this paper cites.
Self-supervised graph transformer on large-scale molecular data
Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; and Huang, J. 2020 · 2007
Earlier work this paper cites.
970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
Blum, L. C.; and Reymond, J.-L. 2009 · 2009
Earlier work this paper cites.
Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S.; Grand, G.; and Ramsundar, B. 2020 · 2010
Earlier work this paper cites.
3D Protein Surface Segmentation through Mathematical Morphology
Cantoni, V.; Gatti, R.; and Lombardi, L. 2011 · 2011
Earlier work this paper cites.
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Klicpera, J.; Giri, S.; Margraf, J. T.; and Günnemann, S. 2020 · 2011
Earlier work this paper cites.
A Bayesian approach to in silico blood-brain barrier penetration modeling
Martins, I. F.; Teixeira, A. L.; Pinheiro, L.; and Falcao, A. O. 2012 · 2012
Earlier work this paper cites.
ATOM3D: Tasks On Molecules in Three Dimensions
Townshend, R. J.; Vögele, M.; Suriana, P.; Derry, A.; Powers, A.; Laloudakis, Y.; Balachandar, S.; Anderson, B.; Eismann, S.; Kondor, R.; et al. 2020 · 2012
Earlier work this paper cites.
Motif-driven contrastive learning of graph representations
Zhang, S.; Hu, Z.; Subramonian, A.; and Sun, Y. 2020 · 2012
Earlier work this paper cites.
RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Landrum, G. 2013 · 2013
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R.; Dral, P. O.; Rupp, M.; and von Lilienfeld, O. A. 2014 · 2014
Cited alongside, same era.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D.; Maclaurin, D.; Aguilera-Iparraguirre, J.; Gómez-Bombarelli, R.; Hirzel, T.; Aspuru-Guzik, A.; and Adams, R. P. 2015 · 2015
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Electronic spectra from TDDFT and machine learning in chemical space
Ramakrishnan, R.; Hartmann, M.; Tapavicza, E.; and Von Lilienfeld, O. A. 2015 · 2015
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Massively multitask networks for drug discovery
Ramsundar, B.; Kearnes, S.; Riley, P.; Webster, D.; Konerding, D.; and Pande, V. 2015 · 2015
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Generative models for graph-based protein design
Ingraham, J.; Garg, V. K.; Barzilay, R.; and Jaakkola, T. 2019 · 2019
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DeepAffinity: interpretable deep learning of compound–protein affinity through unified recurrent and convolutional neural networks
Karimi, M.; Wu, D.; Wang, Z.; and Shen, Y. 2019 · 2019
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Molecular property prediction: A multilevel quantum interactions modeling perspective
Lu, C.; Liu, Q.; Wang, C.; Huang, Z.; Lin, P.; and He, L. 2019 · 2019
Later among the works it cites.
Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Xiong, Z.; Wang, D.; Liu, X.; Zhong, F.; Wan, X.; Li, X.; Li, Z.; Luo, X.; Chen, K.; Jiang, H.; et al. 2019 · 2019
Later among the works it cites.
Analyzing learned molecular representations for property prediction
Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M.; et al. 2019 · 2019
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Order matters: Sequence to sequence for sets
Vinyals, O.; Bengio, S.; and Kudlur, M. 2015 · 2015
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A data-driven approach to predicting successes and failures of clinical trials
Gayvert, K. M.; Madhukar, N. S.; and Elemento, O. 2016 · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S.; McCloskey, K.; Berndl, M.; Pande, V.; and Riley, P. 2016 · 2016
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Tertiary alphabet for the observable protein structural universe
Mackenzie, C. O.; Zhou, J.; and Grigoryan, G. 2016 · 2016
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Neural message passing for quantum chemistry
Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; and Dahl, G. E. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Cited alongside, same era.
Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Xu, Z.; Wang, S.; Zhu, F.; and Huang, J. 2017 · 2017
Cited alongside, same era.
Protein sequence design with a learned potential
Anand-Achim, N.; Eguchi, R. R.; Mathews, I. I.; Perez, C. P.; Derry, A.; Altman, R. B.; and Huang, P. 2021 · 2020
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Multi-scale self-attention for text classification
Guo, Q.; Qiu, X.; Liu, P.; Xue, X.; and Zhang, Z. 2020 · 2020
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Caster: Predicting drug interactions with chemical substructure representation
Huang, K.; Xiao, C.; Hoang, T.; Glass, L.; and Sun, J. 2020 · 2020
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Hierarchical generation of molecular graphs using structural motifs
Jin, W.; Barzilay, R.; and Jaakkola, T. 2020 · 2020
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Hierarchical, rotation-equivariant neural networks to select structural models of protein complexes
Eismann, S.; Townshend, R. J.; Thomas, N.; Jagota, M.; Jing, B.; and Dror, R. O. 2021 · 2021
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Lietransformer: Equivariant self-attention for lie groups
Hutchinson, M. J.; Le Lan, C.; Zaidi, S.; Dupont, E.; Teh, Y. W.; and Kim, H. 2021 · 2021
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Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation
Ishida, S.; Miyazaki, T.; Sugaya, Y.; and Omachi, S. 2021 · 2021
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Highly accurate protein structure prediction with AlphaFold
Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; et al. 2021 · 2021
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Spherical message passing for 3d graph networks
Liu, Y.; Wang, L.; Liu, M.; Zhang, X.; Oztekin, B.; and Ji, S. 2021 · 2021
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Relative Molecule Self-Attention Transformer
Maziarka, Ł.; Majchrowski, D.; Danel, T.; Gaiński, P.; Tabor, J.; Podolak, I.; Morkisz, P.; and Jastrzebski, S. 2021 · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K. T.; Unke, O. T.; and Gastegger, M. 2021 · 2021
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Multi-Scale Representation Learning on Proteins
Somnath, V. R.; Bunne, C.; and Krause, A. 2021 · 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.-Y. 2021 · 2021
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Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
Zhang, Z.; Liu, Q.; Wang, H.; Lu, C.; and Lee, C.-K. 2021 · 2021
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