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Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery.
Lga – a method for finding 3d similarities in protein structures
Zemla, A · 2003
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Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Behler, J · 2011
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P., Rupp, M., and von Lilienfeld, A · 2014
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Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
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Protein interface prediction using graph convolutional networks
Fout, A., Byrd, J., Shariat, B., and Ben-Hur, A · 2017
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Neural message passing for quantum chemistry, 2017
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, 2017
Schütt, K. T., Kindermans, P.-J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Ani-1, a data set of 20 million calculated off-equilibrium conformations for organic molecules
Smith, J., Isayev, O., and Roitberg, A · 2017
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Attention is all you need, 2017
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Relational inductive biases, deep learning, and graph networks, 2018
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., Gulcehre, C., Song, F., Ballard, A., Gilmer, J., Dahl, G., Vaswani, A., Allen, K., Nash, C., Langston, V., Dyer, C., Heess, N., Wierstra, D., Kohli, P., Botvinick, M., Vinyals, O., Li, Y., and Pascanu, R · 2018
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds, 2018
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
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Graph attention networks, 2018
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2018
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Learning steerable filters for rotation equivariant cnns, 2018
Weiler, M., Hamprecht, F. A., and Storath, M · 2018
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Cormorant: Covariant molecular neural networks, 2019
Anderson, B., Hy, T.-S., and Kondor, R · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context, 2019
Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q. V., and Salakhutdinov, R · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Generative models for graph-based protein design
Ingraham, J., Garg, V., Barzilay, R., and Jaakkola, T · 2019
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Graph matching networks for learning the similarity of graph structured objects, 2019
Li, Y., Gu, C., Dullien, T., Vinyals, O., and Kohli, P · 2019
Exploring self-attention for image recognition, 2020
Zhao, H., Jia, J., and Koltun, V · 2020
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials, 2021
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges, 2021
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale, 2021
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N · 2021
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Rethinking graph transformers with spectral attention, 2021
Kreuzer, D., Beaini, D., Hamilton, W. L., Létourneau, V., and Tossou, P · 2021
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Stand-alone self-attention in vision models, 2019
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., and Shlens, J · 2019
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Generalizing convolutional neural networks for equivariance to lie groups on arbitrary continuous data, 2020
Finzi, M., Stanton, S., Izmailov, P., and Wilson, A. G · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks, 2020
Fuchs, F. B., Worrall, D. E., Fischer, V., and Welling, M · 2020
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Directional message passing for molecular graphs, 2020
Klicpera, J., Groß, J., and Günnemann, S · 2020
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Relevance of rotationally equivariant convolutions for predicting molecular properties, 2020
Miller, B. K., Geiger, M., Smidt, T. E., and Noé, F · 2020
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Equivariant graph neural networks for 3d macromolecular structure, 2021a
Jing, B., Eismann, S., Soni, P. N., and Dror, R. O
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E(n) equivariant graph neural networks, 2021
Satorras, V. G., Hoogeboom, E., and Welling, M · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra, 2021
Schütt, K. T., Unke, O. T., and Gastegger, M · 2021
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Atom3d: Tasks on molecules in three dimensions, 2021
Townshend, R. J. L., Vögele, M., Suriana, P., Derry, A., Powers, A., Laloudakis, Y., Balachandar, S., Jing, B., Anderson, B., Eismann, S., Kondor, R., Altman, R. B., and Dror, R. O · 2021
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Do transformers really perform bad for graph representation?, 2021
Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., and Liu, T.-Y · 2021
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Equivariant transformers for neural network based molecular potentials
Thölke, P. and Fabritiis, G. D · 2022
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Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects
Unke, O. T., Chmiela, S., Gastegger, M., Schütt, K. T., Sauceda, H. E., and Müller, K.-R · 2041
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