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Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic.
Chirality in bioactive agents and its pitfalls
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Enantioselective aspects of drug action and disposition: therapeutic pitfalls
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Rdkit: Open-source cheminformatics
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A review of drug isomerism and its significance
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Rules for the nomenclature of Organic Chemistry: Section E: stereochemistry (Recommendations 1974)
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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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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Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
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A new fundamental type of conformational isomerism
Peter J Canfield, Iain M Blake, Zheng-Li Cai, Ian J Luck, Elmars Krausz, Rika Kobayashi, Jeffrey R Reimers, and Maxwell J Crossley · 2018
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Machine learning in computer-aided synthesis planning
Connor W Coley, William H Green, and Klavs F Jensen · 2018
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Interactive supercomputing on 40,000 cores for machine learning and data analysis
Albert Reuther, Jeremy Kepner, Chansup Byun, Siddharth Samsi, William Arcand, David Bestor, Bill Bergeron, Vijay Gadepally, Michael Houle, Matthew Hubbell, et al · 2018
Fast graph representation learning with pytorch geometric
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Strategies for pre-training graph neural networks
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Ultra-large library docking for discovering new chemotypes
Jiankun Lyu, Sheng Wang, Trent E Balius, Isha Singh, Anat Levit, Yurii S Moroz, Matthew J O’Meara, Tao Che, Enkhjargal Algaa, Kateryna Tolmachova, et al · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Analyzing learned molecular representations for property prediction
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
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Cormorant: Covariant molecular neural networks
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Guacamol: benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin HS Segler, and Alain C Vaucher · 2019
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Deep learning for molecular design—a review of the state of the art
Daniel C Elton, Zois Boukouvalas, Mark D Fuge, and Peter W Chung · 2019
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Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller
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Learning molecular representations for medicinal chemistry
Kangway V Chuang, Laura Gunsalus, and Michael J Keiser · 2020
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Neural message passing on high order paths
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Message passing neural networks
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Drug discovery with explainable artificial intelligence
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Qsar without borders
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