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Shape-based virtual screening is widely employed in ligand-based drug design to search chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures compared to known ligands.
A Gaussian Description of Molecular Shape
J A Grant and B T Pickup · 1995
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A fast method of molecular shape comparison: A simple application of a Gaussian description of molecular shape
J. A. Grant, M. A. Gallardo, and B. T. Pickup · 1996
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Hierarchical Generation of Molecular Graphs using Structural Motifs, April 2020
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2002
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A Shape-Based 3-D Scaffold Hopping Method and Its Application to a Bacterial Protein-Protein Interaction
Thomas S. Rush, J. Andrew Grant, Lidia Mosyak, and Anthony Nicholls · 2005
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Comparison of Shape-Matching and Docking as Virtual Screening Tools
Paul C. D. Hawkins, A. Geoffrey Skillman, and Anthony Nicholls · 2007
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Learning from Protein Structure with Geometric Vector Perceptrons, May 2021
Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael J. L. Townshend, and Ron Dror · 2009
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RDKit: Open-source cheminformatics
Greg Landrum · 2010
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Molecular Shape and Medicinal Chemistry: A Perspective
Anthony Nicholls, Georgia B. McGaughey, Robert P. Sheridan, Andrew C. Good, Gregory Warren, Magali Mathieu, Steven W. Muchmore, Scott P. Brown, J. Andrew Grant, James A. Haigh, Neysa Nevins, Ajay N. Jain, and Brian Kelley · 2010
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Matthew Ragoza, Tomohide Masuda, and David Ryan Koes · 2010
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Symmetry-Aware Actor-Critic for 3D Molecular Design, November 2020b
Gregor N. C. Simm, Robert Pinsler, Gábor Csányi, and José Miguel Hernández-Lobato · 2011
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Recent Advances in Scaffold Hopping
Ye Hu, Dagmar Stumpfe, and Jürgen Bajorath · 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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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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Learning Deep Generative Models of Graphs, March 2018
Yujia Li, Oriol Vinyals, Chris Dyer, Razvan Pascanu, and Peter Battaglia · 2018
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Constrained Graph Variational Autoencoders for Molecule Design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Marwin H. S. Segler, Thierry Kogej, Christian Tyrchan, and Mark P. Waller · 2018
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GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders, February 2018
Martin Simonovsky and Nikos Komodakis · 2018
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Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley · 2018
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GuacaMol: Benchmarking Models for de Novo Molecular Design
Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher · 2019
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Junction Tree Variational Autoencoder for Molecular Graph Generation, March 2019
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2019
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Shape-Based Generative Modeling for de Novo Drug Design
Miha Skalic, José Jiménez, Davide Sabbadin, and Gianni De Fabritiis · 2019
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Dynamic Graph CNN for Learning on Point Clouds, June 2019
Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon · 2019
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Structure- and Ligand-Based Virtual Screening on DUD-E+: Performance Dependence on Approximations to the Binding Pocket
Ann E. Cleves and Ajay N. Jain · 2020
Cited alongside, same era.
Generating Realistic 3D Molecules with an Equivariant Conditional Likelihood Model
James P. Roney, Paul Maragakis, Peter Skopp, and David E. Shaw · 2021
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Deep scaffold hopping with multimodal transformer neural networks
Shuangjia Zheng, Zengrong Lei, Haitao Ai, Hongming Chen, Daiguo Deng, and Yuedong Yang · 2021
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Generative models for molecular discovery: Recent advances and challenges
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F. Jensen · 2022
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MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design, March 2022
Yuanqi Du, Tianfan Fu, Jimeng Sun, and Shengchao Liu · 2022
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Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning, February 2022
Daniel Flam-Shepherd, Alexander Zhigalin, and Alán Aspuru-Guzik · 2022
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SE(3)-transformers: 3D roto-translation equivariant attention networks
Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling · 2020
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A Deep Generative Model for Fragment-Based Molecule Generation
Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Cited alongside, same era.
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, Artur Kadurin, Simon Johansson, Hongming Chen, Sergey Nikolenko, Alán Aspuru-Guzik, and Alex Zhavoronkov · 2020
Cited alongside, same era.
Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches
Javier Vázquez, Manel López, Enric Gibert, Enric Herrero, and F. Javier Luque · 2020
Cited alongside, same era.
MOLUCINATE: A Generative Model for Molecules in 3D Space, November 2021
Michael Arcidiacono and David Ryan Koes · 2021
Cited alongside, same era.
Vector Neurons: A General Framework for SO(3)-Equivariant Networks, April 2021
Congyue Deng, Or Litany, Yueqi Duan, Adrien Poulenard, Andrea Tagliasacchi, and Leonidas Guibas · 2021
Cited alongside, same era.
Structure-aware generation of drug-like molecules, November 2021
Pavol Drotár, Arian Rokkum Jamasb, Ben Day, Cătălina Cangea, and Pietro Liò · 2021
Cited alongside, same era.
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Inverse design of 3d molecular structures with conditional generative neural networks
Niklas W. A. Gebauer, Michael Gastegger, Stefaan S. P. Hessmann, Klaus-Robert Müller, and Kristof T. Schütt · 2022
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Equivariant Diffusion for Molecule Generation in 3D, June 2022
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling · 2022
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3DLinker: An E(3) Equivariant Variational Autoencoder for Molecular Linker Design, May 2022
Yinan Huang, Xingang Peng, Jianzhu Ma, and Muhan Zhang · 2022
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Torsional Diffusion for Molecular Conformer Generation, June 2022
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Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji · 2022
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A 3D Generative Model for Structure-Based Drug Design
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An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch
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Learning to Extend Molecular Scaffolds with Structural Motifs, April 2022
Krzysztof Maziarz, Henry Jackson-Flux, Pashmina Cameron, Finton Sirockin, Nadine Schneider, Nikolaus Stiefl, Marwin Segler, and Marc Brockschmidt · 2022
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Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets, May 2022
Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma · 2022
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Fragment-Based Ligand Generation Guided By Geometric Deep Learning On Protein-Ligand Structure
Alexander S. Powers, Helen H. Yu, Patricia Suriana, and Ron O. Dror · 2022
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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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Deep learning approaches for de novo drug design: An overview
Mingyang Wang, Zhe Wang, Huiyong Sun, Jike Wang, Chao Shen, Gaoqi Weng, Xin Chai, Honglin Li, Dongsheng Cao, and Tingjun Hou · 2022
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