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Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications.
Distance geometry and molecular conformation , volume 74
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Graph convolutional policy network for goal-directed molecular graph generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J · 1988
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Gradient-based optimization of hyperparameters
Bengio, Y · 2000
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Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2001
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Model selection via bilevel optimization
Bennett, K. P., Hu, J., Ji, X., Kunapuli, G., and Pang, J.-S · 2006
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An overview of bilevel optimization
Colson, B., Marcotte, P., and Savard, G · 2007
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Evaluating derivatives: principles and techniques of algorithmic differentiation
Griewank, A. and Walther, A · 2008
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2008
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Large-scale machine learning with stochastic gradient descent
Bottou, L · 2010
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Symmetry-aware actor-critic for 3d molecular design
Simm, G. N., Pinsler, R., Csányi, G., and Hernández-Lobato, J. M · 2011
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Generic methods for optimization-based modeling
Domke, J · 2012
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A kernel two-sample test
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A · 2012
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Determining protein structures from noesy distance constraints by semidefinite programming
Alipanahi, B., Krislock, N., Ghodsi, A., Wolkowicz, H., Donaldson, L., and Li, M · 2013
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Learning constrained task similarities in graphregularized multi-task learning
Flamary, R., Rakotomamonjy, A., and Gasso, G · 2014
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Euclidean distance geometry and applications
Liberti, L., Lavor, C., Maculan, N., and Mucherino, A · 2014
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Exploiting the potential energy landscape to sample free energy
Ballard, A. J., Martiniani, S., Stevenson, J. D., Somani, S., and Wales, D. J · 2015
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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
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Gradient-based hyperparameter optimization through reversible learning
Maclaurin, D., Duvenaud, D., and Adams, R · 2015
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Better informed distance geometry: using what we know to improve conformation generation
Riniker, S. and Landrum, G. A · 2015
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Role of molecular dynamics and related methods in drug discovery
De Vivo, M., Masetti, M., Bottegoni, G., and Cavalli, A · 2016
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End-to-end differentiable learning of protein structure
AlQuraishi, M · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N., Gastegger, M., and Schütt, K · 2019
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Generating valid euclidean distance matrices
Hoffmann, M. and Noé, F · 2019
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Strategies for pre-training graph neural networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2019
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Learning protein structure with a differentiable simulator
Ingraham, J., Riesselman, A. J., Sander, C., and Marks, D. S · 2019
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Forward and reverse gradient-based hyperparameter optimization
Franceschi, L., Donini, M., Frasconi, P., and Pontil, M · 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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Conformation generation: the state of the art
Hawkins, P. C · 2017
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Towards poisoning of deep learning algorithms with back-gradient optimization
Muñoz-González, L., Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Felix, H. E. S., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Encodermap: Dimensionality reduction and generation of molecule conformations
Lemke, T. and Peter, C · 2019
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Molecular geometry prediction using a deep generative graph neural network
Mansimov, E., Mahmood, O., Kang, S., and Cho, K · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al · 2019
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Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Axelrod, S. and Gomez-Bombarelli, R · 2020
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Torsionnet: A reinforcement learning approach to sequential conformer search
Gogineni, T., Xu, Z., Punzalan, E., Jiang, R., Kammeraad, J., Tewari, A., and Zimmerman, P · 2020
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High accuracy protein structure prediction using deep learning
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Tunyasuvunakool, K., Ronneberger, O., Bates, R., Zidek, A., Bridgland, A., et al · 2020
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A generative model for molecular distance geometry
Simm, G. N. and Hernández-Lobato, J. M · 2020
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Psi4 1.4: Open-source software for high-throughput quantum chemistry
Smith, D. G., Burns, L. A., Simmonett, A. C., Parrish, R. M., Schieber, M. C., Galvelis, R., Kraus, P., Kruse, H., Di Remigio, R., Alenaizan, A., et al · 2020
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Active learning and neural network potentials accelerate molecular screening of ether-based solvate ionic liquids
Wang, W., Yang, T., Harris, W. H., Gomez-Bombarelli, R., and Gómez-Bombarelli, R · 2020
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Learning neural generative dynamics for molecular conformation generation
Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J · 2021
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