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We present GPS++, a hybrid Message Passing Neural Network / Graph Transformer model for molecular property prediction.
Self-consistent equations including exchange and correlation effects
Kohn, W. and Sham, L. J · 1965
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Chemical space and biology
Dobson, C. M · 2004
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and Von Lilienfeld, O. A · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Ba, J. L., Kiros, J. R., and Hinton, G. E · 2016
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Gaussian error linear units (GELUs)
Hendrycks, D. and Gimpel, K · 2016
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Deep networks with stochastic depth
Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K. Q · 2016
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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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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., and Wu, H · 2017
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Pubchemqc project: a large-scale first-principles electronic structure database for data-driven chemistry
Nakata, M. and Shimazaki, T · 2017
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Schnet - a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P. J., 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. S., Isayev, O., and Roitberg, A. E · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I · 2017
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Relational inductive biases, deep learning, and graph networks
Battaglia, P. W., Hamrick, J. B., Bapst, V., Sanchez-Gonzalez, A., Zambaldi, V., Malinowski, M., Tacchetti, A., Raposo, D., Santoro, A., Faulkner, R., et al · 2018
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Huang, Y., Cheng, Y., Bapna, A., Firat, O., Chen, M. X., Chen, D., Lee, H., Ngiam, J., Le, Q. V., Wu, Y., and Chen, Z · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Li, Q., Han, Z., and Wu, X · 2018
Cited alongside, same era.
Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R · 2018
Cited alongside, same era.
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 · 2018
Cited alongside, same era.
Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
Cited alongside, same era.
Reducing down(stream)time: Pretraining molecular gnns using heterogeneous ai accelerators
Bilbrey, J. A., Herman, K. M., Sprueill, H., Xantheas, S. S., Das, P., Roldan, M. L., Kraus, M., Helal, H., and Choudhury, S · 2022
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Graph neural networks with learnable structural and positional representations
Dwivedi, V. P., Luu, A. T., Laurent, T., Bengio, Y., and Bresson, X · 2022
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Simple GNN regularisation for 3D molecular property prediction and beyond
Godwin, J., Schaarschmidt, M., Gaunt, A. L., Sanchez-Gonzalez, A., Rubanova, Y., Veličković, P., Kirkpatrick, J., and Battaglia, P · 2022
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Artificial intelligence foundation for therapeutic science
Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., and Zitnik, M · 2022
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Global self-attention as a replacement for graph convolution
Hussain, M. S., Zaki, M. J., and Subramanian, D · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Dwivedi, V. P. and Bresson, X · 2020
Cited alongside, same era.
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
Cited alongside, same era.
Grover: Self-supervised message passing transformer on large-scale molecular data
Rong, Y., Bian, Y., Xu, T., yang Xie, W., Wei, Y., bing Huang, W., and Huang, J · 2020
Cited alongside, same era.
On the bottleneck of graph neural networks and its practical implications
Alon, U. and Yahav, E · 2021
Cited alongside, same era.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Bronstein, M. M., Bruna, J., Cohen, T., and Veličković, P · 2021
Cited alongside, same era.
OGB-LSC: A large-scale challenge for machine learning on graphs
Hu, W., Fey, M., Ren, H., Nakata, M., Dong, Y., and Leskovec, J · 2021
Cited alongside, same era.
Combining machine learning and computational chemistry for predictive insights into chemical systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., and Tkatchenko, A · 2021
Cited alongside, same era.
Pure transformers are powerful graph learners
Kim, J., Nguyen, T. D., Min, S., Cho, S., Lee, M., Lee, H., and Hong, S · 2022
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Liu, L., He, D., Fang, X., Zhang, S., Wang, F., He, J., and Wu, H · 2022
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One transformer can understand both 2D & 3D molecular data
Luo, S., Chen, T., Xu, Y., Zheng, S., Liu, T.-Y., Wang, L., and He, D · 2022
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GRPE: Relative positional encoding for graph transformer
Park, W., Chang, W., Lee, D., Kim, J., and won Hwang, S · 2022
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Recipe for a General, Powerful, Scalable Graph Transformer
Rampášek, L., Galkin, M., Dwivedi, V. P., Luu, A. T., Wolf, G., and Beaini, D · 2022
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Graph neural networks for materials science and chemistry
Reiser, P., Neubert, M., Eberhard, A., Torresi, L., Zhou, C., Shao, C., Metni, H., van Hoesel, C., Schopmans, H., Sommer, T., et al · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Shi, Y., Zheng, S., Ke, G., Shen, Y., You, J., He, J., Luo, S., Liu, C., He, D., and Liu, T.-Y · 2022
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Torchmd-net: Equivariant transformers for neural network based molecular potentials
Thölke, P. and De Fabritiis, G · 2022
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The open catalyst 2022 (oc22) dataset and challenges for oxide electrocatalysis
Tran, R., Lan, J., Shuaibi, M., Goyal, S., Wood, B. M., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., et al · 2022
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How to better introduce geometric information in equivariant message passing?
Wang, Y., Li, S., Wang, T., Wang, Z., He, X., Shao, B., and Liu, T.-Y · 2022
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Pre-training via denoising for molecular property prediction
Zaidi, S., Schaarschmidt, M., Martens, J., Kim, H., Teh, Y. W., Sanchez-Gonzalez, A., Battaglia, P., Pascanu, R., and Godwin, J · 2022
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Everything is connected: Graph neural networks
Veličković, P · 2023
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