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Generating molecules that bind to specific proteins is an important but challenging task in drug discovery.
Structure-based strategies for drug design and discovery
Irwin D Kuntz · 1992
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UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Anthony K Rappé, Carla J Casewit, KS Colwell, William A Goddard III, and W Mason Skiff · 1992
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Drug discovery: a historical perspective
Jurgen Drews · 2000
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RDKit: Open-source cheminformatics
Greg Landrum et al · 2006
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Protein-protein docking using region-based 3d zernike descriptors
Vishwesh Venkatraman, Yifeng Yang, Lee Sael, and Daisuke Kihara · 2009
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Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 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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Density estimation using real NVP
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Tutorial on variational autoencoders
Carl Doersch · 2016
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Semi-supervised classification with graph convolutional networks, 2016
Thomas N. Kipf and Max Welling · 2016
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Neural message passing for quantum chemistry, 2017
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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ChemTS: an efficient python library for de novo molecular generation
Xiufeng Yang, Jinzhe Zhang, Kazuki Yoshizoe, Kei Terayama, and Koji Tsuda · 2017
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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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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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Dr.Regina Barzilay, and Tommi Jaakkola · 2020
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Equivariant flows: Exact likelihood generative learning for symmetric densities, 2020
Jonas Köhler, Leon Klein, and Frank Noé · 2020
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Tomohide Masuda, Matthew Ragoza, and David Ryan Koes · 2020
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Survae flows: Surjections to bridge the gap between vaes and flows
Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, and Max Welling · 2020
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Molecular sets (MOSES): a benchmarking platform for molecular generation models
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al · 2020
Cited alongside, same era.
GraphAF: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2020
Cited alongside, same era.
MoFlow: an invertible flow model for generating molecular graphs
Chengxi Zang and Fei Wang · 2020
Cited alongside, same era.
Learning from protein structure with geometric vector perceptrons
Anonymous · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models, 2021
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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E(n) equivariant normalizing flows, 2021
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B. Fuchs, Ingmar Posner, and Max Welling · 2021
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E(n) equivariant graph neural networks, 2021
Victor 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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Fast end-to-end learning on protein surfaces
Freyr Sverrisson, Jean Feydy, Bruno E. Correia, and Michael M. Bronstein · 2021
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Atom3d: Tasks on molecules in three dimensions
Raphael John Lamarre Townshend, Martin Vögele, Patricia Adriana Suriana, Alexander Derry, Alexander Powers, Yianni Laloudakis, Sidhika Balachandar, Bowen Jing, Brandon M Anderson, Stephan Eismann, et al · 2021
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Structured denoising diffusion models in discrete state-spaces, 2021
Jacob Austin, Daniel D. Johnson, Jonathan Ho, Daniel Tarlow, and Rianne van den Berg · 2021
Cited alongside, same era.
Accurate prediction of protein structures and interactions using a three-track neural network
Minkyung Baek, Frank DiMaio, Ivan Anishchenko, Justas Dauparas, Sergey Ovchinnikov, Gyu Rie Lee, Jue Wang, Qian Cong, Lisa N. Kinch, R. Dustin Schaeffer, Claudia Millán, Hahnbeom Park, Carson Adams, Caleb R. Glassman, Andy DeGiovanni, Jose H. Pereira, Andria V. Rodrigues, Alberdina A. van Dijk, Ana C. Ebrecht, Diederik J. Opperman, Theo Sagmeister, Christoph Buhlheller, Tea Pavkov-Keller, Manoj K. Rathinaswamy, Udit Dalwadi, Calvin K. Yip, John E. Burke, K. Christopher Garcia, Nick V. Grishin, Paul D. Adams, Randy J. Read, and David Baker · 2021
Cited alongside, same era.
Yoshua Bengio, Tristan Deleu, Edward J. Hu, Salem Lahlou, Mo Tiwari, and Emmanuel Bengio · 2021
Cited alongside, same era.
Protein complex prediction with alphafold-multimer
Richard Evans, Michael O’Neill, Alexander Pritzel, Natasha Antropova, Andrew Senior, Tim Green, Augustin Žídek, Russ Bates, Sam Blackwell, Jason Yim, Olaf Ronneberger, Sebastian Bodenstein, Michal Zielinski, Alex Bridgland, Anna Potapenko, Andrew Cowie, Kathryn Tunyasuvunakool, Rishub Jain, Ellen Clancy, Pushmeet Kohli, John Jumper, and Demis Hassabis · 2021
Cited alongside, same era.
Independent se(3)-equivariant models for end-to-end rigid protein docking
Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, and Andreas Krause · 2021
Cited alongside, same era.
Computed structures of core eukaryotic protein complexes
Ian R. Humphreys, Jimin Pei, Minkyung Baek, Aditya Krishnakumar, Ivan Anishchenko, Sergey Ovchinnikov, Jing Zhang, Travis J. Ness, Sudeep Banjade, Saket R. Bagde, Viktoriya G. Stancheva, Xiao-Han Li, Kaixian Liu, Zhi Zheng, Daniel J. Barrero, Upasana Roy, Jochen Kuper, Israel S. Fernández, Barnabas Szakal, Dana Branzei, Josep Rizo, Caroline Kisker, Eric C. Greene, Sue Biggins, Scott Keeney, Elizabeth A. Miller, J. Christopher Fromme, Tamara L. Hendrickson, Qian Cong, and David Baker · 2021
Cited alongside, same era.
Equivariant graph neural networks for 3d macromolecular structure, 2021
Bowen Jing, Stephan Eismann, Pratham N. Soni, and Ron O. Dror · 2021
Cited alongside, same era.
Later among the works it cites.
Self-supervised learning on graphs: Contrastive, generative,or predictive
Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan Z. Li · 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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Molecule3D: A benchmark for predicting 3d geometries from molecular graphs
Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, and Shuiwang Ji · 2021
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Unleashing transformers: Parallel token prediction with discrete absorbing diffusion for fast high-resolution image generation from vector-quantized codes
Sam Bond-Taylor, Peter Hessey, Hiroshi Sasaki, Toby P. Breckon, and Chris G. Willcocks · 2022
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A survey on generative diffusion model
Hanqun Cao, Cheng Tan, Zhangyang Gao, Guangyong Chen, Pheng-Ann Heng, and Stan Z Li · 2022
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Robust deep learning based protein sequence design using proteinmpnn
J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, and D. Baker · 2022
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MolGenSurvey: A systematic survey in machine learning models for molecule design, 2022
Yuanqi Du, Tianfan Fu, Jimeng Sun, and Shengchao Liu · 2022
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Alphadesign: A graph protein design method and benchmark on alphafolddb
Zhangyang Gao, Cheng Tan, Stan Li, et al · 2022
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Equivariant diffusion for molecule generation in 3d, 2022
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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Generating 3d molecules for target protein binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, 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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Target-aware molecular graph generation
Cheng Tan, Zhangyang Gao, and Stan Z Li · 2022
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Deep generative molecular design reshapes drug discovery
Xiangxiang Zeng, Fei Wang, Yuan Luo, Seung gu Kang, Jian Tang, Felice C. Lightstone, Evandro F. Fang, Wendy Cornell, Ruth Nussinov, and Feixiong Cheng · 2022
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