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Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
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Merck molecular force field. v. extension of mmff94 using experimental data, additional computational data, and empirical rules
Thomas A Halgren · 1996
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Non-equilibrium thermodynamics
Sybren Ruurds De Groot and Peter Mazur · 2013
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Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2013
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Euclidean distance geometry and applications
Leo Liberti, Carlile Lavor, Nelson Maculan, and Antonio Mucherino · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Better informed distance geometry: Using what we know to improve conformation generation
Sereina Riniker and Gregory A. Landrum · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Density estimation using Real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Conformation generation: the state of the art
Paul CD Hawkins · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Tensor field networks: Rotation- and translation-equivariant neural networks for 3d point clouds
N. Thomas, T. Smidt, Steven M. Kearnes, Lusann Yang, L. Li, Kai Kohlhoff, and P. Riley · 2018
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3d steerable cnns: Learning rotationally equivariant features in volumetric data
M. Weiler, M. Geiger, M. Welling, W. Boomsma, and T. Cohen · 2018
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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Linfeng Zhang, Jiequn Han, Han Wang, Roberto Car, and Weinan E · 2018
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End-to-end differentiable learning of protein structure
Mohammed AlQuraishi · 2019
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Learning protein structure with a differentiable simulator
John Ingraham, Adam J Riesselman, Chris Sander, and Debora S Marks · 2019
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Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
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A generative model for molecular distance geometry
Gregor Simm and Jose Miguel Hernandez-Lobato · 2020
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Psi4 1.4: Open-source software for high-throughput quantum chemistry
Daniel G. A. Smith, L. Burns, A. Simmonett, R. Parrish, M. C. Schieber, Raimondas Galvelis, P. Kraus, H. Kruse, Roberto Di Remigio, Asem Alenaizan, A. M. James, S. Lehtola, Jonathon P Misiewicz, et al · 2020
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2020
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Improved techniques for training score-based generative models
Yang Song and Stefano Ermon · 2020
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Se (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Tess E Smidt, Lixin Sun, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, and Boris Kozinsky · 2021
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2020
Cited alongside, same era.
Implicit functions in feature space for 3d shape reconstruction and completion
Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll · 2020
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Se(3)-transformers: 3d roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
Cited alongside, same era.
Torsionnet: A reinforcement learning approach to sequential conformer search
T. Gogineni, Ziping Xu, Exequiel Punzalan, Runxuan Jiang, Joshua A Kammeraad, Ambuj Tewari, and P. Zimmerman · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Geomol: Torsional geometric generation of molecular 3d conformer ensembles
Octavian-Eugen Ganea, Lagnajit Pattanaik, Connor W Coley, Regina Barzilay, Klavs F Jensen, William H Green, and Tommi S Jaakkola · 2021
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Inverse design of 3d molecular structures with conditional generative neural networks
Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert Müller, and Kristof T Schütt · 2021
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Forcenet: A graph neural network for large-scale quantum chemistry simulation
Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and Larry Zitnick · 2021
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Learning from protein structure with geometric vector perceptrons
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael John Lamarre Townshend, and Ron Dror · 2021
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Highly accurate protein structure prediction with alphafold
John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al · 2021
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Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
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Predicting molecular conformation via dynamic graph score matching
Shitong Luo, Chence Shi, Minkai Xu, and Jian Tang · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Rotation invariant graph neural networks using spin convolutions
Muhammed Shuaibi, Adeesh Kolluru, Abhishek Das, Aditya Grover, Anuroop Sriram, Zachary Ulissi, and C Lawrence Zitnick · 2021
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Symmetry-aware actor-critic for 3d molecular design
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 2021
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Direct molecular conformation generation
Jinhua Zhu, Yingce Xia, Chang Liu, Lijun Wu, Shufang Xie, Tong Wang, Yusong Wang, Wengang Zhou, Tao Qin, Houqiang Li, et al · 2022
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