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Efficient design and discovery of target-driven molecules is a critical step in facilitating lead optimization in drug discovery.
The properties of known drugs. 1. molecular frameworks
Guy W. Bemis and Mark A. Murcko · 1996
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Prediction of Physicochemical Parameters by Atomic Contributions
Scott A. Wildman and Gordon M. Crippen · 1999
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ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
John S. Delaney · 2004
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Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions
Peter Ertl and Ansgar Schuffenhauer · 2009
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Quantifying the chemical beauty of drugs
G. Richard Bickerton, Gaia V. Paolini, Jérémy Besnard, Sorel Muresan, and Andrew L. Hopkins · 2012
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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 · 2012
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BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology
Michael K. Gilson, Tiqing Liu, Michael Baitaluk, George Nicola, Linda Hwang, and Jenny Chong · 2016
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 2018
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Molecular generative model based on conditional variational autoencoder for de novo molecular design
Jaechang Lim, Seongok Ryu, Jin Woo Kim, and Woo Youn Kim · 2018
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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt · 2018
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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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Schnet – a deep learning architecture for molecules and materials
K. T. Schütt, H. E. Sauceda, P.-J. Kindermans, A. Tkatchenko, and K.-R. Müller · 2018
Cited alongside, same era.
De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping
Target-specific and selective drug design for covid-19 using deep generative models
Vijil Chenthamarakshan, Payel Das, Inkit Padhi, Hendrik Strobelt, Kar Wai Lim, Ben Hoover, Samuel C Hoffman, and Aleksandra Mojsilovic · 2020
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Deeppurpose: a deep learning based drug repurposing toolkit
Kexin Huang, Tianfan Fu, Cao Xiao, Lucas Glass, and Jimeng Sun · 2020
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Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder
Sunghoon Joo, Min Soo Kim, Jaeho Yang, and Jeahyun Park · 2020
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Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2
Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, Nikita Janakarajan, Antonio Cardinale, Teodoro Laino, and María Rodríguez Martínez · 2021
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Boris Sattarov, Igor I. Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, and Alexandre Varnek · 2019
Cited alongside, same era.
Schnetpack: A deep learning toolbox for atomistic systems
K. T. Schütt, P. Kessel, M. Gastegger, K. A. Nicoli, A. Tkatchenko, and K.-R. Müller · 2019
Cited alongside, same era.
Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
Niclas Ståhl, Göran Falkman, Alexander Karlsson, Gunnar Mathiason, and Jonas Boström · 2019
Cited alongside, same era.
Jannis Born, Matteo Manica, Joris Cadow, Greta Markert, Nil Adell Mill, Modestas Filipavicius, and María Rodríguez Martínez · 2020
Cited alongside, same era.
URL http://zinc.docking.org/substances/subsets/fda/?page=1
Zinc database
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High-throughput virtual screening and validation of a sars-cov-2 main protease noncovalent inhibitor
Austin Clyde, Stephanie Galanie, Daniel W Kneller, Heng Ma, Yadu Babuji, Ben Blaiszik, Alexander Brace, Thomas Brettin, Kyle Chard, Ryan Chard, et al · 2021
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3d-scaffold: Deep learning framework to generate 3d coordinates of drug-like molecules with desired scaffolds
Rajendra Prashad Joshi, Niklas Gebauer, Neeraj Kumar, and Mridula Bontha · 2021
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Decoding the protein-ligand interactions using parallel graph neural networks
Carter Knutson, Mridula Bontha, Jenna A Bilbrey, and Neeraj Kumar · 2021
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Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2041
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