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Structure-based drug design, i.e., finding molecules with high affinities to the target protein pocket, is one of the most critical tasks in drug discovery.
Using shape complementarity as an initial screen in designing ligands for a receptor binding site of known three-dimensional structure
Renee L DesJarlais, Robert P Sheridan, George L Seibel, J Scott Dixon, Irwin D Kuntz, and R Venkataraghavan · 1988
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3D QSAR in drug design: volume 1: theory methods and applications
Hugo Kubinyi · 1993
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Smog: de novo design method based on simple, fast, and accurate free energy estimates. 1. methodology and supporting evidence
Robert S DeWitte and Eugene I Shakhnovich · 1996
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Smog: de novo design method based on simple, fast, and accurate free energy estimates. 2. case studies in molecular design
Robert S DeWitte, Alexey V Ishchenko, and Eugene I Shakhnovich · 1997
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Virtual screening—an overview
W Patrick Walters, Matthew T Stahl, and Mark A Murcko · 1998
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Fast, efficient generation of high-quality atomic charges. am1-bcc model: I. method
Araz Jakalian, Bruce L Bush, David B Jack, and Christopher I Bayly · 2000
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A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations
Yong Duan, Chun Wu, Shibasish Chowdhury, Mathew C Lee, Guoming Xiong, Wei Zhang, Rong Yang, Piotr Cieplak, Ray Luo, Taisung Lee, et al · 2003
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Virtual screening of chemical libraries
Brian K Shoichet · 2004
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Exploring protein native states and large-scale conformational changes with a modified generalized born model
Alexey Onufriev, Donald Bashford, and David A Case · 2004
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Screening in a spirit haunted world
Brian K Shoichet · 2006
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Free energy calculations
Christophe Chipot and Andrew Pohorille · 2007
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Comparative assessment of scoring functions on a diverse test set
Tiejun Cheng, Xun Li, Yan Li, Zhihai Liu, and Renxiao Wang · 2009
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Blast+: architecture and applications
Christiam Camacho, George Coulouris, Vahram Avagyan, Ning Ma, Jason Papadopoulos, Kevin Bealer, and Thomas L Madden · 2009
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Basic ingredients of free energy calculations: a review
Clara D Christ, Alan E Mark, and Wilfred F Van Gunsteren · 2010
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Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations
Julien Michel and Jonathan W Essex · 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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Free energy calculations of protein–ligand interactions
Anita de Ruiter and Chris Oostenbrink · 2011
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lddt: a local superposition-free score for comparing protein structures and models using distance difference tests
Valerio Mariani, Marco Biasini, Alessandro Barbato, and Torsten Schwede · 2013
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Ligsift: an open-source tool for ligand structural alignment and virtual screening
Ambrish Roy and Jeffrey Skolnick · 2015
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The mm/pbsa and mm/gbsa methods to estimate ligand-binding affinities
Samuel Genheden and Ulf Ryde · 2015
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3d u-net: Learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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The Character of Physical Law, with new foreword
Richard Feynman · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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3dmolnet: a generative network for molecular structures
Vitali Nesterov, Mario Wieser, and Volker Roth · 2020
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Reinforcement learning for molecular design guided by quantum mechanics
Gregor Simm, Robert Pinsler, and José Miguel Hernández-Lobato · 2020
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De novo molecule design through the molecular generative model conditioned by 3d information of protein binding sites
Mingyuan Xu, Ting Ran, and Hongming Chen · 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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Attention-based generative models for de novo molecular design
Orion Dollar, Nisarg Joshi, David AC Beck, and Jim Pfaendtner · 2021
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Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L Guimaraes, and Alan Aspuru-Guzik · 2017
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Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
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Molecular generation with recurrent neural networks (rnns)
Esben Jannik Bjerrum and Richard Threlfall · 2017
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Syntax-directed variational autoencoder for structured data
Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song · 2018
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Marwin HS Segler, Thierry Kogej, Christian Tyrchan, and Mark P Waller · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
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Keeping it simple: Language models can learn complex molecular distributions
Daniel Flam-Shepherd, Kevin Zhu, and Alán Aspuru-Guzik · 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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Autodock vina 1.2. 0: New docking methods, expanded force field, and python bindings
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli · 2021
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Deepfrag: An open-source browser app for deep-learning lead optimization
Harrison Green and Jacob D Durrant · 2021
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Generating 3d molecules conditional on receptor binding sites with deep generative models
Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 2022
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A 3d molecule generative model for structure-based drug design
Shitong Luo, Jiaqi Guan, Jianzhu Ma, and Jian Peng · 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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Uni-gbsa: An automatic workflow to perform mm/gb(pb)sa calculations for virtual screening
Maohua Yang, Dongdong Wang, and Hang Zheng · 2022
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2022
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Benchmarking graphormer on large-scale molecular modeling datasets
Yu Shi, Shuxin Zheng, Guolin Ke, Yifei Shen, Jiacheng You, Jiyan He, Shengjie Luo, Chang Liu, Di He, and Tie-Yan Liu · 2022
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Molecular property prediction and molecular design using a supervised grammar variational autoencoder
André F Oliveira, Juarez LF Da Silva, and Marcos G Quiles · 2022
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Equivariant diffusion for molecule generation in 3d
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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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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