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Deep generative diffusion models are a promising avenue for 3D de novo molecular design in materials science and drug discovery.
PubChem3D: a new resource for scientists
Evan E. Bolton, Jie Chen, Sunghwan Kim, Lianyi Han, Siqian He, Wenyao Shi, Vahan Simonyan, Yan Sun, Paul A. Thiessen, Jiyao Wang, Bo Yu, Jian Zhang, and Stephen H. Bryant · 2011
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Conformer generation with omega: Learning from the data set and the analysis of failures
Paul C. D. Hawkins and Anthony Nicholls · 2012
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O. Dral, Pavlo O. Dral, Matthias Rupp, and O. Anatole von Lilienfeld · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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Protein-ligand blind docking using QuickVina-W with inter-process spatio-temporal integration
Nafisa M Hassan, Amr A Alhossary, Yuguang Mu, and Chee-Keong Kwoh · 2017
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Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Christoph Bannwarth, Sebastian Ehlert, and Stefan Grimme · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas Gebauer, Michael Gastegger, and Kristof Schütt · 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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Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
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Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design
Paul G. Francoeur, Tomohide Masuda, Jocelyn Sunseri, Andrew Jia, Richard B. Iovanisci, Ian Snyder, and David R. Koes · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noe · 2020
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A generative model for molecular distance geometry
Gregor Simm and Jose Miguel Hernandez-Lobato · 2020
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Structured denoising diffusion models in discrete state-spaces
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
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Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M. Bronstein, Joan Bruna, Taco Cohen, and Petar Velivckovi’c · 2021
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Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling
Valentin De Bortoli, James Thornton, Jeremy Heng, and Arnaud Doucet · 2021
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Diffusion models beat GANs on image synthesis
Prafulla Dhariwal and Alexander Quinn Nichol · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, and Max Welling · 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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On density estimation with diffusion models
Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Diffwave: A versatile diffusion model for audio synthesis
Zhifeng Kong, Wei Ping, Jiaji Huang, Kexin Zhao, and Bryan Catanzaro · 2021
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A 3d generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Representation learning on biomolecular structures using equivariant graph attention
Tuan Le, Frank Noe, and Djork-Arné Clevert · 2022
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Diffusion-LM improves controllable text generation
Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, and Tatsunori Hashimoto · 2022
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An autoregressive flow model for 3d molecular geometry generation from scratch
Youzhi Luo and Shuiwang Ji · 2022
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Pocket2Mol: Efficient molecular sampling based on 3D protein pockets
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Progressive distillation for fast sampling of diffusion models
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Grad-tts: A diffusion probabilistic model for text-to-speech
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E(n) equivariant normalizing flows
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof Schütt, Oliver Unke, and Michael Gastegger · 2021
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E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E. Smidt, and Boris Kozinsky · 2022
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Riemannian Score-Based Generative Modelling
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EquiBind: Geometric deep learning for drug binding structure prediction
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Equivariant transformers for neural network based molecular potentials
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Diffusion-based molecule generation with informative prior bridges
Lemeng Wu, Chengyue Gong, Xingchao Liu, Mao Ye, and qiang liu · 2022
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Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2022
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi S. Jaakkola · 2023
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Structure-based drug design with equivariant diffusion models, 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
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