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We devise an approach for targeted molecular design, a problem of interest in computational drug discovery: given a target protein site, we wish to generate a chemical with both high binding affinity to the target and satisfactory pharmacological properties.
SMILES, a line notation and computerized interpreter for chemical structures
Eric Anderson, Gilman D Veith, and David Weininger · 1987
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The art and practice of structure-based drug design: A molecular modeling perspective
Regine S Bohacek, Colin McMartin, and Wayne C Guida · 1996
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Knowledge-based scoring function to predict protein-ligand interactions1
Holger Gohlke, Manfred Hendlich, and Gerhard Klebe · 2000
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1
Christopher A Lipinski, Franco Lombardo, Beryl W Dominy, and Paul J Feeney · 2001
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Molecular targeting therapy of cancer: drug resistance, apoptosis and survival signal
Takashi Tsuruo, Mikihiko Naito, Akihiro Tomida, Naoya Fujita, Tetsuo Mashima, Hiroshi Sakamoto, and Naomi Haga · 2003
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Ucsf chimera—a visualization system for exploratory research and analysis
Eric F Pettersen, Thomas D Goddard, Conrad C Huang, Gregory S Couch, Daniel M Greenblatt, Elaine C Meng, and Thomas E Ferrin · 2004
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Can the pharmaceutical industry reduce attrition rates?
Ismail Kola and John Landis · 2004
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Drugscorecsd knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better affinity prediction
Hans FG Velec, Holger Gohlke, and Gerhard Klebe · 2005
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Mechanisms of disease: oncogene addiction—a rationale for molecular targeting in cancer therapy
I Bernard Weinstein and Andrew K Joe · 2006
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Computational methods in developing quantitative structure-activity relationships (qsar): a review
Arkadiusz Z Dudek, Tomasz Arodz, and Jorge Gálvez · 2006
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Rdkit: Open-source cheminformatics, 2006
Greg Landrum et al · 2006
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sc-pdb: an annotated database of druggable binding sites from the protein data bank
Esther Kellenberger, Pascal Muller, Claire Schalon, Guillaume Bret, Nicolas Foata, and Didier Rognan · 2006
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The influence of drug-like concepts on decision-making in medicinal chemistry
Paul D Leeson and Brian Springthorpe · 2007
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A constructive approach for discovering new drug leads: Using a kernel methodology for the inverse-qsar problem
William WL Wong and Forbes J Burkowski · 2009
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Biopython: freely available python tools for computational molecular biology and bioinformatics
Peter J. A. Cock, Tiago Antao, Jeffrey T. Chang, Brad A. Chapman, Cymon J. Cox, Andrew Dalke, Iddo Friedberg, Thomas Hamelryck, Frank Kauff, Bartek Wilczynski, and Michiel J. L. de Hoon · 2009
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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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How to improve r&d productivity: the pharmaceutical industry’s grand challenge
Steven M Paul, Daniel S Mytelka, Christopher T Dunwiddie, Charles C Persinger, Bernard H Munos, Stacy R Lindborg, and Aaron L Schacht · 2010
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3d-qsar in drug design-a review
Jitender Verma, Vijay M Khedkar, and Evans C Coutinho · 2010
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Exhaustive structure generation for inverse-qspr/qsar
Tomoyuki Miyao, Masamoto Arakawa, and Kimito Funatsu · 2010
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Drawing the pdb: protein- ligand complexes in two dimensions
Katrin Stierand and Matthias Rarey · 2010
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Scoring functions and their evaluation methods for protein–ligand docking: recent advances and future directions
Sheng-You Huang, Sam Z Grinter, and Xiaoqin Zou · 2010
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Are scoring functions in protein- protein docking ready to predict interactomes? clues from a novel binding affinity benchmark
Panagiotis L Kastritis and Alexandre MJJ Bonvin · 2010
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Lipophilicity in drug discovery
Michael J Waring · 2010
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sc-pdb: a database for identifying variations and multiplicity of ‘druggable’binding sites in proteins
Jamel Meslamani, Didier Rognan, and Esther Kellenberger · 2011
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Dsx: a knowledge-based scoring function for the assessment of protein–ligand complexes
Gerd Neudert and Gerhard Klebe · 2011
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Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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The enumeration of chemical space
Jean-Louis Reymond, Lars Ruddigkeit, Lorenz Blum, and Ruud van Deursen · 2012
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Diagnosing the decline in pharmaceutical r&d efficiency
Jack W Scannell, Alex Blanckley, Helen Boldon, and Brian Warrington · 2012
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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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Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
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The holistic integration of virtual screening in drug discovery
Yusuf Tanrikulu, Björn Krüger, and Ewgenij Proschak · 2013
Cited alongside, same era.
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E Turner, and Douglas Eck · 2016
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Chemical-space-based de novo design method to generate drug-like molecules
Shunichi Takeda, Hiromasa Kaneko, and Kimito Funatsu · 2016
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Democratizing deep-learning for drug discovery, quantum chemistry, materials science and biology
DeepChem · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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The rcsb protein data bank: integrative view of protein, gene and 3d structural information
Peter W Rose, Andreas Prlić, Ali Altunkaya, Chunxiao Bi, Anthony R Bradley, Cole H Christie, Luigi Di Costanzo, Jose M Duarte, Shuchismita Dutta, Zukang Feng, et al · 2016
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Cited alongside, same era.
sc-pdb: a 3d-database of ligandable binding sites—10 years on
Jérémy Desaphy, Guillaume Bret, Didier Rognan, and Esther Kellenberger · 2014
Cited alongside, same era.
Recent improvements to binding moad: a resource for protein–ligand binding affinities and structures
Aqeel Ahmed, Richard D Smith, Jordan J Clark, James B Dunbar Jr, and Heather A Carlson · 2014
Cited alongside, same era.
Challenges, applications, and recent advances of protein-ligand docking in structure-based drug design
Sam Z Grinter and Xiaoqin Zou · 2014
Cited alongside, same era.
Improved protein–ligand binding affinity prediction by using a curvature-dependent surface-area model
Yang Cao and Lei Li · 2014
Cited alongside, same era.
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
Cited alongside, same era.
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Tox21challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs
Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada A Shahane, Anna Rossoshek, and Anton Simeonov · 2016
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Is it reliable to use common molecular docking methods for comparing the binding affinities of enantiomer pairs for their protein target?
David Ramírez and Julio Caballero · 2016
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Protein–ligand scoring with convolutional neural networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, and David Ryan Koes · 2017
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Chemgan challenge for drug discovery: can ai reproduce natural chemical diversity?
Mostapha Benhenda · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Constrained bayesian optimization for automatic chemical design
Ryan-Rhys Griffiths · 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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Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic), Aug 2017
Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L. Guimaraes, and Alan Aspuru-Guzik · 2017
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drugan: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico
Artur Kadurin, Sergey Nikolenko, Kuzma Khrabrov, Alex Aliper, and Alex Zhavoronkov · 2017
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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 · 2017
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Application of generative autoencoder in de novo molecular design
Thomas Blaschke, Marcus Olivecrona, Ola Engkvist, Jürgen Bajorath, and Hongming Chen · 2017
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Protein data bank (pdb): the single global macromolecular structure archive
Stephen K Burley, Helen M Berman, Gerard J Kleywegt, John L Markley, Haruki Nakamura, and Sameer Velankar · 2017
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Learning a generative model for validity in complex discrete structures
David Janz, Jos van der Westhuizen, Brooks Paige, Matt J Kusner, and Jose Miguel Hernandez-Labato · 2017
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Protein interface prediction using graph convolutional networks
Alex Fout, Jonathon Byrd, Basir Shariat, and Asa Ben-Hur · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 2017
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The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018
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Planning chemical syntheses with deep neural networks and symbolic ai
Marwin HS Segler, Mike Preuss, and Mark P Waller · 2018
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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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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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David Ramírez and Julio Caballero · 2018
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