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Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery.
Feature trees: a new molecular similarity measure based on tree matching
Matthias Rarey and J Scott Dixon · 1998
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Rdkit: Open-source cheminformatics
Greg Landrum et al · 2006
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The scaffold tree- visualization of the scaffold universe by hierarchical scaffold classification
Ansgar Schuffenhauer, Peter Ertl, Silvio Roggo, Stefan Wetzel, Marcus A Koch, and Herbert Waldmann · 2007
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On the art of compiling and using’drug-like’chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 2008
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Computational methods for scaffold hopping
Ansgar Schuffenhauer · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Rectifier nonlinearities improve neural network acoustic models
Andrew L Maas, Awni Y Hannun, and Andrew Y Ng · 2013
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Get your atoms in order - an open-source implementation of a novel and robust molecular canonicalization algorithm
Nadine Schneider, Roger A Sayle, and Gregory A Landrum · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Generating sentences from a continuous space
Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew Dai, Rafal Jozefowicz, and Samy Bengio · 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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Defactor: Differentiable edge factorization-based probabilistic graph generation
Rim Assouel, Mohamed Ahmed, Marwin H Segler, Amir Saffari, and Yoshua Bengio · 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 N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 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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Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
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Frechet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Günter Klambauer · 2018
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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 · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Scaffold-constrained molecular generation
Maxime Langevin, Hervé Minoux, Maximilien Levesque, and Marc Bianciotto · 2020
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Graph networks for molecular design
Rocío Mercado, Tobias Rastemo, Edvard Lindelöf, Günter Klambauer, Ola Engkvist, Hongming Chen, and Esben Jannik Bjerrum · 2020
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Comparison of atom representations in graph neural networks for molecular property prediction
Agnieszka Pocha, Tomasz Danel, and Łukasz Maziarka · 2020
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On failure modes of molecule generators and optimizers
Philipp Renz, Dries Van Rompaey, Jörg Kurt Wegner, Sepp Hochreiter, and Günter Klambauer · 2020
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Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Rex Ying, Vijay Pande, and Jure Leskovec · 2018
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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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Deepscaffold: A comprehensive tool for scaffold-based de novo drug discovery using deep learning
Yibo Li, Jianxing Hu, Yanxing Wang, Jielong Zhou, Liangren Zhang, and Zhenming Liu · 2019
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Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L Hamilton, David Duvenaud, Raquel Urtasun, and Richard S Zemel · 2019
Cited alongside, same era.
Scaffold-based molecular design using graph generative model
Jaechang Lim, Sang-Yeon Hwang, Seungsu Kim, Seokhyun Moon, and Woo Youn Kim · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Mikołaj Sacha, Mikołaj Błaż, Piotr Byrski, Paweł Włodarczyk-Pruszyński, and Stanisław Jastrzębski · 2020
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Reinforced molecular optimization with neighborhood-controlled grammars
Chencheng Xu, Qiao Liu, Minlie Huang, and Tao Jiang · 2020
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Pubchem compound summary for cid 10182969, apixaban
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