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Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by studying the structure that they take in 3D space.
Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid
James D Watson and Francis HC Crick · 1953
Earlier work this paper cites.
Survey sampling
Leslie Kish · 1965
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Normal distribution on the rotation group so (3)
Dmitry I Nikolayev and Tatjana I Savyolov · 1970
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General definition of ring puckering coordinates
D t Cremer and JA Pople · 1975
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Reverse-time diffusion equation models
Brian DO Anderson · 1982
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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A limited memory algorithm for bound constrained optimization
Richard H Byrd, Peihuang Lu, Jorge Nocedal, and Ciyou Zhu · 1995
Earlier work this paper cites.
Merck molecular force field. i. basis, form, scope, parameterization, and performance of mmff94
Thomas A Halgren · 1996
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Annealed importance sampling
Radford M Neal · 2001
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How important is parity violation for molecular and biomolecular chirality?
Martin Quack · 2002
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Announcing the worldwide Protein Data Bank
H. Berman, K. Henrick, and H. Nakamura · 2003
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Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening
Thomas A Halgren, Robert B Murphy, Richard A Friesner, Hege S Beard, Leah L Frye, W Thomas Pollard, and Jay L Banks · 2004
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The backrub motion: how protein backbone shrugs when a sidechain dances
Ian W Davis, W Bryan Arendall III, David C Richardson, and Jane S Richardson · 2006
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Moldock: a new technique for high-accuracy molecular docking
René Thomsen and Mikael H Christensen · 2006
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Caesar: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration
Jiabo Li, Tedman Ehlers, Jon Sutter, Shikha Varma-O’Brien, and Johannes Kirchmair · 2007
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The exploration of macrocycles for drug discovery—an underexploited structural class
Edward M Driggers, Stephen P Hale, Jinbo Lee, and Nicholas K Terrett · 2008
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Dg-ammos: A new tool to generate 3d conformation of small molecules using d istance g eometry and a utomated m olecular m echanics o ptimization for in silico s creening
David Lagorce, Tania Pencheva, Bruno O Villoutreix, and Maria A Miteva · 2009
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Conformer generation with omega: algorithm and validation using high quality structures from the protein databank and cambridge structural database
Paul CD Hawkins, A Geoffrey Skillman, Gregory L Warren, Benjamin A Ellingson, and Matthew T Stahl · 2010
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Frog2: Efficient 3d conformation ensemble generator for small compounds
Maria A Miteva, Frederic Guyon, and Pierre Tuffery · 2010
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Autodock vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Oleg Trott and Arthur J Olson · 2010
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Pubchem3d: conformer generation
Evan E Bolton, Sunghwan Kim, and Stephen H Bryant · 2011
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Stochastic models, information theory, and Lie groups, volume 2: Analytic methods and modern applications
Gregory S Chirikjian · 2011
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Automated minimization of steric clashes in protein structures
Srinivas Ramachandran, Pradeep Kota, Feng Ding, and Nikolay V Dokholyan · 2011
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Conformer generation with omega: learning from the data set and the analysis of failures
Paul CD Hawkins and Anthony Nicholls · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Lessons learned in empirical scoring with smina from the csar 2011 benchmarking exercise
David Ryan Koes, Matthew P Baumgartner, and Carlos J Camacho · 2013
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Greg Landrum et al · 2013
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Fast, accurate, and reliable molecular docking with QuickVina 2
Amr Alhossary, Stephanus Daniel Handoko, Yuguang Mu, and Chee-Keong Kwoh · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 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 Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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On implementing 2d rectangular assignment algorithms
David F Crouse · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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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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Conformation generation: the state of the art
Paul CD Hawkins · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2017
Cited alongside, same era.
Forging the basis for developing protein–ligand interaction scoring functions
Zhihai Liu, Minyi Su, Li Han, Jie Liu, Qifan Yang, Yan Li, and Renxiao Wang · 2017
Cited alongside, same era.
Software for molecular docking: a review
Nataraj S Pagadala, Khajamohiddin Syed, and Jack Tuszynski · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Knowledge-based conformer generation using the cambridge structural database
Bootstrap your flow
Laurence Illing Midgley, Vincent Stimper, Gregor NC Simm, and José Miguel Hernández-Lobato · 2021
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Critical assessment of protein intrinsic disorder prediction
Marco Necci, Damiano Piovesan, and Silvio CE Tosatto · 2021
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Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
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Noise estimation for generative diffusion models
Robin San-Roman, Eliya Nachmani, and Lior Wolf · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 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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Jason C Cole, Oliver Korb, Patrick McCabe, Murray G Read, and Robin Taylor · 2018
Cited alongside, same era.
P2rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure
Radoslav Krivák and David Hoksza · 2018
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Spacetime and geometry
Sean M Carroll · 2019
Cited alongside, same era.
Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
Cited alongside, same era.
Functional maps representation on product manifolds
Emanuele Rodolà, Zorah Lähner, Alexander M Bronstein, Michael M Bronstein, and Justin Solomon · 2019
Cited alongside, same era.
Multi-scale representation learning on proteins
Vignesh Ram Somnath, Charlotte Bunne, and Andreas Krause · 2021
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Learning to efficiently sample from diffusion probabilistic models
Daniel Watson, Jonathan Ho, Mohammad Norouzi, and William Chan · 2021
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How neural networks extrapolate: From feedforward to graph neural networks
Keyulu Xu, Mozhi Zhang, Jingling Li, Simon S Du, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2021
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Learning neural generative dynamics for molecular conformation generation
Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, and Jian Tang · 2021
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An end-to-end framework for molecular conformation generation via bilevel programming
Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, and Jian Tang · 2021
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Geodiff: A geometric diffusion model for molecular conformation generation
Minkai Xu, Lantao Yu, Yang Song, Chence Shi, Stefano Ermon, and Jian Tang · 2021
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gómez-Bombarelli · 2022
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Analytic-dpm: an analytic estimate of the optimal reverse variance in diffusion probabilistic models
Fan Bao, Chongxuan Li, Jun Zhu, and Bo Zhang · 2022
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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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Improving de novo protein binder design with deep learning
Nathaniel Bennett, Brian Coventry, Inna Goreshnik, Buwei Huang, Aza Allen, Dionne Vafeados, Ying Po Peng, Justas Dauparas, Minkyung Baek, Lance Stewart, et al · 2022
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Riemannian score-based generative modeling
Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, and Arnaud Doucet · 2022
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e3nn: Euclidean neural networks
Mario Geiger and Tess Smidt · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clement Vignac, and Max Welling · 2022
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Illuminating protein space with a programmable generative model
John Ingraham, Max Baranov, Zak Costello, Vincent Frappier, Ahmed Ismail, Shan Tie, Wujie Wang, Vincent Xue, Fritz Obermeyer, Andrew Beam, et al · 2022
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Subspace diffusion generative models
Bowen Jing, Gabriele Corso, Renato Berlinghieri, and Tommi Jaakkola · 2022
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Torsional diffusion for molecular conformer generation
Bowen Jing, Gabriele Corso, Jeffrey Chang, Regina Barzilay, and Tommi Jaakkola · 2022
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Denoising diffusion probabilistic models on so(3) for rotational alignment
Adam Leach, Sebastian M Schmon, Matteo T Degiacomi, and Chris G Willcocks · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Allan dos Santos Costa, Maryam Fazel-Zarandi, Tom Sercu, Sal Candido, and Alexander Rives · 2022
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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng · 2022
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Pignet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, and Woo Youn Kim · 2022
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Progressive distillation for fast sampling of diffusion models
Tim Salimans and Jonathan Ho · 2022
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Equibind: Geometric deep learning for drug binding structure prediction
Hannes Stärk, Octavian Ganea, Lagnajit Pattanaik, Regina Barzilay, and Tommi Jaakkola · 2022
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On the frustration to predict binding affinities from protein–ligand structures with deep neural networks
Mikhail Volkov, Joseph-André Turk, Nicolas Drizard, Nicolas Martin, Brice Hoffmann, Yann Gaston-Mathé, and Didier Rognan · 2022
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Structure-aware multimodal deep learning for drug–protein interaction prediction
Penglei Wang, Shuangjia Zheng, Yize Jiang, Chengtao Li, Junhong Liu, Chang Wen, Atanas Patronov, Dahong Qian, Hongming Chen, and Yuedong Yang · 2022
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Benchmarking alphafold-enabled molecular docking predictions for antibiotic discovery
Felix Wong, Aarti Krishnan, Erica J Zheng, Hannes Stärk, Abigail L Manson, Ashlee M Earl, Tommi Jaakkola, and James J Collins · 2022
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Protein structure generation via folding diffusion
Kevin E Wu, Kevin K Yang, Rianne van den Berg, James Y Zou, Alex X Lu, and Ava P Amini · 2022
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Diffdock: Diffusion steps, twists, and turns for molecular docking
Gabriele Corso, Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2023
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