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Forming a molecular candidate set that contains a wide range of potentially effective compounds is crucial to the success of drug discovery.
The distribution of the flora in the alpine zone. 1
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Brian K Shoichet, Irwin D Kuntz, and Dale L Bodian · 1992
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Comparison of algorithms for dissimilarity-based compound selection
Michael Snarey, Nicholas K Terrett, Peter Willett, and David J Wilton · 1997
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A proof of the triangle inequality for the tanimoto distance
Alan H Lipkus · 1999
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Hui Fang, Tao Tao, and ChengXiang Zhai · 2004
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Zinc- a free database of commercially available compounds for virtual screening
John J Irwin and Brian K Shoichet · 2005
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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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Computational topology: an introduction
Herbert Edelsbrunner and John Harer · 2010
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Estimation of adme properties with substructure pattern recognition
Jie Shen, Feixiong Cheng, You Xu, Weihua Li, and Yun Tang · 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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Rational methods for the selection of diverse screening compounds
David John Huggins, Ashok Ramakrishnan Venkitaraman, and David Robert Spring · 2011
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Alex Kulesza and Ben Taskar · 2011
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Determinantal point processes for machine learning
Alex Kulesza, Ben Taskar, et al · 2012
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Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
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Jorge Alcalde-Unzu and Marc Vorsatz · 2013
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Paul R Halmos · 2013
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How diverse are diversity assessment methods? a comparative analysis and benchmarking of molecular descriptor space
Alexios Koutsoukas, Shardul Paricharak, Warren RJD Galloway, David R Spring, Adriaan P IJzerman, Robert C Glen, David Marcus, and Andreas Bender · 2014
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rdock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids
Sergio Ruiz-Carmona, Daniel Alvarez-Garcia, Nicolas Foloppe, A Beatriz Garmendia-Doval, Szilveszter Juhos, Peter Schmidtke, Xavier Barril, Roderick E Hubbard, and S David Morley · 2014
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Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling
Mathias J Wawer, Kejie Li, Sigrun M Gustafsdottir, Vebjorn Ljosa, Nicole E Bodycombe, Melissa A Marton, Katherine L Sokolnicki, Mark-Anthony Bray, Melissa M Kemp, Ellen Winchester, et al · 2014
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Why is tanimoto index an appropriate choice for fingerprint-based similarity calculations?
Dávid Bajusz, Anita Rácz, and Károly Héberger · 2015
A note on the triangle inequality for the jaccard distance
Sven Kosub · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Mariya Popova, Mykhailo Shvets, Junier Oliva, and Olexandr Isayev · 2019
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How size matters: diversity for fragment library design
Yun Shi and Mark von Itzstein · 2019
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Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
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Memory-assisted reinforcement learning for diverse molecular de novo design
Thomas Blaschke, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2020
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Multi-objective molecule generation using interpretable substructures
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Cited alongside, same era.
The chembl database in 2017
Anna Gaulton, Anne Hersey, Michał Nowotka, A. Patrícia Bento, Jon Chambers, David Mendez, Prudence Mutowo, Francis Atkinson, Louisa J. Bellis, Elena Cibrián-Uhalte, Mark Davies, Nathan Dedman, Anneli Karlsson, María Paula Magariños, John P. Overington, George Papadatos, Ines Smit, and Andrew R. Leach · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 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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Screening library design
Stephanie Kay Ashenden · 2018
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MolGAN: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer Nansean Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan Prescott Adams, and Alan Aspuru-Guzik · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 2018
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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, et al · 2020
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Vae-sim: a novel molecular similarity measure based on a variational autoencoder
Soumitra Samanta, Steve O’Hagan, Neil Swainston, Timothy J Roberts, and Douglas B Kell · 2020
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Generative models for automatic chemical design
Daniel Schwalbe-Koda and Rafael Gómez-Bombarelli · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Chence Shi, Minkai Xu, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, and Jian Tang · 2020
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Smiles-based qsar and molecular docking study of xanthone derivatives as α \alpha -glucosidase inhibitors
Shahin Ahmadi, Zohreh Moradi, Ashwani Kumar, and Ali Almasirad · 2021
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Generative chemistry: drug discovery with deep learning generative models
Yuemin Bian and Xiang-Qun Xie · 2021
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G-rmsd: Root mean square deviation based method for three-dimensional molecular similarity determination
Tomonori Fukutani, Kohei Miyazawa, Satoru Iwata, and Hiroko Satoh · 2021
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Mars: Markov molecular sampling for multi-objective drug discovery
Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li · 2021
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Axiomatic Analysis of Unsupervised Diversity on Large-Scale High-dimensional Data
Shiyan Yan · 2021
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Comparative study of deep generative models on chemical space coverage
Jie Zhang, Rocío Mercado, Ola Engkvist, and Hongming Chen · 2021
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Artificial intelligence in drug discovery: applications and techniques
Jianyuan Deng, Zhibo Yang, Iwao Ojima, Dimitris Samaras, and Fusheng Wang · 2022
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Differentiable scaffolding tree for molecule optimization
Tianfan Fu, Wenhao Gao, Cao Xiao, Jacob Yasonik, Connor W. Coley, and Jimeng Sun · 2022
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Predicting protein–ligand docking structure with graph neural network
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Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design
AkshatKumar Nigam, Robert Pollice, and Alán Aspuru-Guzik · 2022
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