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Designing ligand-binding proteins, such as enzymes and biosensors, is essential in bioengineering and protein biology.
Ion-pairs in proteins
David J Barlow and JM Thornton · 1983
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The nature of. pi.-. pi. interactions
Christopher A Hunter and Jeremy KM Sanders · 1990
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Numerical solution of initial-value problems in differential-algebraic equations
Kathryn Eleda Brenan, Stephen L Campbell, and Linda Ruth Petzold · 1995
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Cation- π \pi interactions in chemistry and biology: a new view of benzene, phe, tyr, and trp
Dennis A Dougherty · 1996
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The enzyme database in 2000
Amos Bairoch · 2000
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Binding moad (mother of all databases)
Liegi Hu, Mark L Benson, Richard D Smith, Michael G Lerner, and Heather A Carlson · 2005
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The pdbbind database: methodologies and updates
Renxiao Wang, Xueliang Fang, Yipin Lu, Chao-Yie Yang, and Shaomeng Wang · 2005
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Recent progress in understanding hydrophobic interactions
Emily E Meyer, Kenneth J Rosenberg, and Jacob Israelachvili · 2006
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New algorithms and an in silico benchmark for computational enzyme design
Alexandre Zanghellini, Lin Jiang, Andrew M Wollacott, Gong Cheng, Jens Meiler, Eric A Althoff, Daniela Röthlisberger, and David Baker · 2006
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Optimizing fragment and scaffold docking by use of molecular interaction fingerprints
Gilles Marcou and Didier Rognan · 2007
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De novo computational design of retro-aldol enzymes
Lin Jiang, Eric A. Althoff, Fernando R. Clemente, Lindsey Doyle, Daniela Röthlisberger, Alexandre Zanghellini, Jasmine L. Gallaher, Jamie L. Betker, Fujie Tanaka, Carlos F. Barbas, Donald Hilvert, Kendall N. Houk, Barry L. Stoddard, and David Baker · 2008
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Kemp elimination catalysts by computational enzyme design
Daniela Röthlisberger, Olga Khersonsky, Andrew M Wollacott, Lin Jiang, Jason DeChancie, Jamie Betker, Jasmine L Gallaher, Eric A Althoff, Alexandre Zanghellini, Orly Dym, et al · 2008
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Hydrogen bonds in proteins: role and strength
Roderick E Hubbard and Muhammad Kamran Haider · 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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Salt bridges: Geometrically specific, designable interactions
Jason E Donald, Daniel W Kulp, and William F DeGrado · 2011
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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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Hydrophobic interactions
Arieh Y Ben-Naim · 2012
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Computational design of ligand-binding proteins with high affinity and selectivity
Christine E Tinberg, Sagar D Khare, Jiayi Dou, Lindsey Doyle, Jorgen W Nelson, Alberto Schena, Wojciech Jankowski, Charalampos G Kalodimos, Kai Johnsson, Barry L Stoddard, et al · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Plip: fully automated protein–ligand interaction profiler
Sebastian Salentin, Sven Schreiber, V Joachim Haupt, Melissa F Adasme, and Michael Schroeder · 2015
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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The rosetta all-atom energy function for macromolecular modeling and design
Rebecca F Alford, Andrew Leaver-Fay, Jeliazko R Jeliazkov, Matthew J O’Meara, Frank P DiMaio, Hahnbeom Park, Maxim V Shapovalov, P Douglas Renfrew, Vikram K Mulligan, Kalli Kappel, et al · 2017
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Computational design of environmental sensors for the potent opioid fentanyl
Matthew J Bick, Per J Greisen, Kevin J Morey, Mauricio S Antunes, David La, Banumathi Sankaran, Luc Reymond, Kai Johnsson, June I Medford, and David Baker · 2017
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Sampling and energy evaluation challenges in ligand binding protein design
Jiayi Dou, Lindsey Doyle, Per Jr Greisen, Alberto Schena, Hahnbeom Park, Kai Johnsson, Barry L Stoddard, and David Baker · 2017
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Mmseqs2 enables sensitive protein sequence searching for the analysis of massive data sets
Martin Steinegger and Johannes Söding · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Accurate estimation of ligand binding affinity changes upon protein mutation
Matteo Aldeghi, Vytautas Gapsys, and Bert L de Groot · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Benchmarking of different molecular docking methods for protein-peptide docking
Piyush Agrawal, Harinder Singh, Hemant Kumar Srivastava, Sandeep Singh, Gaurav Kishore, and Gajendra PS Raghava · 2019
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Apixaban: a clinical pharmacokinetic and pharmacodynamic review
Wonkyung Byon, Samira Garonzik, Rebecca A Boyd, and Charles E Frost · 2019
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Computational design of a modular protein sense-response system
Anum A Glasgow, Yao-Ming Huang, Daniel J Mandell, Michael Thompson, Ryan Ritterson, Amanda L Loshbaugh, Jenna Pellegrino, Cody Krivacic, Roland A Pache, Kyle A Barlow, et al · 2019
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Blind tests of rna–protein binding affinity prediction
Kalli Kappel, Inga Jarmoskaite, Pavanapuresan P Vaidyanathan, William J Greenleaf, Daniel Herschlag, and Rhiju Das · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
A defined structural unit enables de novo design of small-molecule–binding proteins
Nicholas F Polizzi and William F DeGrado · 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.
Generating novel, designable, and diverse protein structures by equivariantly diffusing oriented residue clouds
Yeqing Lin and Mohammed AlQuraishi · 2023
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Evolutionary-scale prediction of atomic-level protein structure with a language model
Zeming Lin, Halil Akin, Roshan Rao, Brian Hie, Zhongkai Zhu, Wenting Lu, Nikita Smetanin, Robert Verkuil, Ori Kabeli, Yaniv Shmueli, et al · 2023
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Pocketoptimizer 2.0: A modular framework for computer-aided ligand-binding design
Jakob Noske, Josef Paul Kynast, Dominik Lemm, Steffen Schmidt, and Birte Höcker · 2023
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Harmonic self-conditioned flow matching for multi-ligand docking and binding site design
Hannes Stärk, Bowen Jing, Regina Barzilay, and Tommi Jaakkola · 2023
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Brian L. Trippe, Jason Yim, Doug Tischer, David Baker, Tamara Broderick, Regina Barzilay, and Tommi S. Jaakkola · 2023
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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, et al · 2021
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Equivariant graph neural networks for 3d macromolecular structure
Bowen Jing, Stephan Eismann, Pratham N Soni, and Ron O 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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A deep-learning framework for multi-level peptide–protein interaction prediction
Yipin Lei, Shuya Li, Ziyi Liu, Fangping Wan, Tingzhong Tian, Shao Li, Dan Zhao, and Jianyang Zeng · 2021
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A 3d generative model for structure-based drug design
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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De novo design of protein structure and function with rfdiffusion
Joseph L Watson, David Juergens, Nathaniel R Bennett, Brian L Trippe, Jason Yim, Helen E Eisenach, Woody Ahern, Andrew J Borst, Robert J Ragotte, Lukas F Milles, et al · 2023
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De novo design of modular peptide-binding proteins by superhelical matching
Kejia Wu, Hua Bai, Ya-Ting Chang, Rachel Redler, Kerrie E McNally, William Sheffler, TJ Brunette, Derrick R Hicks, Tomos E Morgan, Tim J Stevens, et al · 2023
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De novo design of luciferases using deep learning
Andy Hsien-Wei Yeh, Christoffer Norn, Yakov Kipnis, Doug Tischer, Samuel J Pellock, Declan Evans, Pengchen Ma, Gyu Rie Lee, Jason Z Zhang, Ivan Anishchenko, et al · 2023
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Fast protein backbone generation with se (3) flow matching
Jason Yim, Andrew Campbell, Andrew YK Foong, Michael Gastegger, José Jiménez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S Veeling, Regina Barzilay, Tommi Jaakkola, et al · 2023
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Se (3) diffusion model with application to protein backbone generation
Jason Yim, Brian L Trippe, Valentin De Bortoli, Emile Mathieu, Arnaud Doucet, Regina Barzilay, and Tommi Jaakkola · 2023
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Learning on topological surface and geometric structure for 3d molecular generation
Odin Zhang, Tianyue Wang, Gaoqi Weng, Dejun Jiang, Ning Wang, Xiaorui Wang, Huifeng Zhao, Jialu Wu, Ercheng Wang, Guangyong Chen, et al · 2023
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Diffpack: A torsional diffusion model for autoregressive protein side-chain packing
Yangtian Zhang, Zuobai Zhang, Bozitao Zhong, Sanchit Misra, and Jian Tang · 2023
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Learning subpocket prototypes for generalizable structure-based drug design
Zaixi Zhang and Qi Liu · 2023
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Full-atom protein pocket design via iterative refinement
Zaixi Zhang, Zepu Lu, Hao Zhongkai, Marinka Zitnik, and Qi Liu · 2023
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A systematic survey in geometric deep learning for structure-based drug design
Zaixi Zhang, Jiaxian Yan, Qi Liu, Enhong Chen, and Marinka Zitnik · 2023
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Accurate structure prediction of biomolecular interactions with alphafold 3
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Se (3)-stochastic flow matching for protein backbone generation
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De novo design of drug-binding proteins with predictable binding energy and specificity
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