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Ligand-based drug design aims to identify novel drug candidates of similar shapes with known active molecules.
On Information and Sufficiency
Kullback, S.; and Leibler, R. A. 1951 · 1951
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Comparison of Shape-Matching and Docking as Virtual Screening Tools
Hawkins, P. C. D.; Skillman, A. G.; and Nicholls, A. 2006 · 2006
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Accelerating 3D Deep Learning with PyTorch3D
Ravi, N.; Reizenstein, J.; Novotny, D.; Gordon, T.; Lo, W.-Y.; Johnson, J.; and Gkioxari, G. 2020 · 2007
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ShaEP: Molecular Overlay Based on Shape and Electrostatic Potential
Vainio, M. J.; Puranen, J. S.; and Johnson, M. S. 2009 · 2009
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Recent Advances in Ligand-Based Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore Approach
Acharya, C.; Coop, A.; Polli, J. E.; and MacKerell, A. D. 2011 · 2011
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State-of-the-art in ligand-based virtual screening
Ripphausen, P.; Nisius, B.; and Bajorath, J. 2011 · 2011
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The future of crystallography in drug discovery
Zheng, H.; Hou, J.; Zimmerman, M. D.; Wlodawer, A.; and Minor, W. 2013 · 2013
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Adam: A Method for Stochastic Optimization
Kingma, D. P.; and Ba, J. 2015 · 2015
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Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field
Wójcikowski, M.; Zielenkiewicz, P.; and Siedlecki, P. 2015 · 2015
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Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R.; Wei, J. N.; Duvenaud, D.; Hernández-Lobato, J. M.; Sánchez-Lengeling, B.; Sheberla, D.; Aguilera-Iparraguirre, J.; Hirzel, T. D.; Adams, R. P.; and Aspuru-Guzik, A. 2018 · 2018
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Jin, W.; Barzilay, R.; and Jaakkola, T. 2018 · 2018
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A Structure-Based Drug Discovery Paradigm
Batool, M.; Ahmad, B.; and Choi, S. 2019 · 2019
Cited alongside, same era.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Park, J. J.; Florence, P.; Straub, J.; Newcombe, R.; and Lovegrove, S. 2019 · 2019
Cited alongside, same era.
Dynamic Graph CNN for Learning on Point Clouds
Wang, Y.; Sun, Y.; Liu, Z.; Sarma, S. E.; Bronstein, M. M.; and Solomon, J. M. 2019 · 2019
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D.; Zhebrak, A.; Sanchez-Lengeling, B.; Golovanov, S.; Tatanov, O.; Belyaev, S.; Kurbanov, R.; Artamonov, A.; Aladinskiy, V.; Veselov, M.; Kadurin, A.; Johansson, S.; Chen, H.; Nikolenko, S.; Aspuru-Guzik, A.; and Zhavoronkov, A. 2020 · 2020
Cited alongside, same era.
A deep generative model for molecule optimization via one fragment modification
A 3D Generative Model for Structure-Based Drug Design
Luo, S.; Guan, J.; Ma, J.; and Peng, J. 2021 · 2021
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Improved Denoising Diffusion Probabilistic Models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
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De novo design with deep generative models based on 3D similarity scoring
Papadopoulos, K.; Giblin, K. A.; Janet, J. P.; Patronov, A.; and Engkvist, O. 2021 · 2021
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3D Equivariant Graph Implicit Functions
Chen, Y.; Fernando, B.; Bilen, H.; Nießner, M.; and Gavves, E. 2022 · 2022
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Equivariant Diffusion for Molecule Generation in 3D
Hoogeboom, E.; Satorras, V. G.; Vignac, C.; and Welling, M. 2022 · 2022
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Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
Peng, X.; Luo, S.; Guan, J.; Xie, Q.; Peng, J.; and Ma, J. 2022 · 2022
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Chen, Z.; Min, M. R.; Parthasarathy, S.; and Ning, X. 2021 · 2021
Cited alongside, same era.
Vector Neurons: A General Framework for SO(3)-Equivariant Networks
Deng, C.; Litany, O.; Duan, Y.; Poulenard, A.; Tagliasacchi, A.; and Guibas, L. J. 2021 · 2021
Cited alongside, same era.
E(n) Equivariant Normalizing Flows
Garcia Satorras, V.; Hoogeboom, E.; Fuchs, F.; Posner, I.; and Welling, M. 2021 · 2021
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Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions
Hoogeboom, E.; Nielsen, D.; Jaini, P.; Forré, P.; and Welling, M. 2021 · 2021
Cited alongside, same era.
Deep generative design with 3D pharmacophoric constraints
Imrie, F.; Hadfield, T. E.; Bradley, A. R.; and Deane, C. M. 2021 · 2021
Cited alongside, same era.
DiffWave: A Versatile Diffusion Model for Audio Synthesis
Kong, Z.; Ping, W.; Huang, J.; Zhao, K.; and Catanzaro, B. 2021 · 2021
Cited alongside, same era.
Later among the works it cites.
Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design
Adams, K.; and Coley, C. W. 2023 · 2023
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3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction
Guan, J.; Qian, W. W.; Peng, X.; Su, Y.; Peng, J.; and Ma, J. 2023 · 2023
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rdkit/rdkit: 2023_03_2 (Q1 2023) Release
Landrum, G.; Tosco, P.; Kelley, B.; Ric; Cosgrove, D.; Sriniker; Gedeck; Vianello, R.; NadineSchneider; Kawashima, E.; N, D.; Jones, G.; Dalke, A.; Cole, B.; Swain, M.; Turk, S.; AlexanderSavelyev; Vaucher, A.; Wójcikowski, M.; Ichiru Take; Probst, D.; Ujihara, K.; Scalfani, V. F.; Godin, G.; Lehtivarjo, J.; Pahl, A.; Walker, R.; Francois Berenger; Jasondbiggs; and Strets123. 2023 · 2023
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MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
Peng, X.; Guan, J.; Liu, Q.; and Ma, J. 2023 · 2023
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