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In the space of only a few years, deep generative modeling has revolutionized how we think of artificial creativity, yielding autonomous systems which produce original images, music, and text.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams and David Zipser · 1989
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
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Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
Christopher A. Lipinski, Franco Lombardo, Beryl W. Dominy, and Paul J. Feeney · 1997
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A global geometric framework for nonlinear dimensionality reduction
J. B. Tenenbaum · 2000
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Simple selection criteria for drug-like chemical matter
Ingo Muegge, Sarah L. Heald, and David Brittelli · 2001
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Reoptimization of mdl keys for use in drug discovery
Joseph L. Durant, Burton A. Leland, Douglas R. Henry, and James G. Nourse · 2002
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Selection criteria for drug-like compounds
Ingo Muegge · 2003
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A comprehensive listing of bioactivation pathways of organic functional groups
Amit Kalgutkar, Iain Gardner, R. Obach, Christopher Shaffer, Ernesto Callegari, Kirk Henne, Abdul Mutlib, Deepak Dalvie, Jae Lee, Yasuhiro Nakai, John O’Donnell, Jason Boer, and Shawn Harriman · 2005
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Reducing the dimensionality of data with neural networks
Geoffrey Hinton and Ruslan Salakhutdinov · 2006
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Natural product-likeness score and its application for prioritization of compound libraries
Peter Ertl, Silvio Roggo, and Ansgar Schuffenhauer · 2008
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
Lorenz C. Blum and Jean-Louis Reymond · 2009
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Deep Boltzmann machines
Ruslan Salakhutdinov and Geoffrey Hinton · 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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Assessing synthetic accessibility of chemical compounds using machine learning methods
Yevgeniy Podolyan, Michael A. Walters, and George Karypis · 2010
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Impact of high-throughput screening in biomedical research
Ricardo Macarron, Martyn N. Banks, Dejan Bojanic, David J. Burns, Dragan A. Cirovic, Tina Garyantes, Darren V. S. Green, Robert P. Hertzberg, William P. Janzen, Jeff W. Paslay, Ulrich Schopfer, and G. Sitta Sittampalam · 2011
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The quest for novel chemical matter and the contribution of computer-aidedde novodesign
Bernard Pirard · 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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Multi-column deep neural networks for image classification
D. Cireşan, U. Meier, and J. Schmidhuber · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2012
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Automated design of ligands to polypharmacological profiles
Jérémy Besnard, Gian Filippo Ruda, Vincent Setola, Keren Abecassis, Ramona M. Rodriguiz, Xi-Ping Huang, Suzanne Norval, Maria F. Sassano, Antony I. Shin, Lauren A. Webster, Frederick R. C. Simeons, Laste Stojanovski, Annik Prat, Nabil G. Seidah, Daniel B. Constam, G. Richard Bickerton, Kevin D. Read, William C. Wetsel, Ian H. Gilbert, Bryan L. Roth, and Andrew L. Hopkins · 2012
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Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Jean-Louis Reymond · 2012
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A few useful things to know about machine learning
Pedro Domingos · 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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Estimation of the size of drug-like chemical space based on GDB-17 data
P. G. Polishchuk, T. I. Madzhidov, and A. Varnek · 2013
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Auto-Encoding Variational Bayes
D. P Kingma and M. Welling · 2013
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InChI - the worldwide chemical structure identifier standard
Stephen Heller, Alan McNaught, Stephen Stein, Dmitrii Tchekhovskoi, and Igor Pletnev · 2013
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Multi-task Neural Networks for QSAR Predictions
George E. Dahl, Navdeep Jaitly, and Ruslan Salakhutdinov · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Discovering chemistry with an ab initio nanoreactor
Lee-Ping Wang, Alexey Titov, Robert McGibbon, Fang Liu, Vijay S. Pande, and Todd J. Martínez · 2014
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Efficient enumeration of monocyclic chemical graphs with given path frequencies
Masaki Suzuki, Hiroshi Nagamochi, and Tatsuya Akutsu · 2014
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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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Learning phrase representations using rnn encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Sequence to Sequence Learning with Neural Networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Neural Turing Machines
A. Graves, G. Wayne, and I. Danihelka · 2014
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Prediction of synthetic accessibility based on commercially available compound databases
Yoshifumi Fukunishi, Takashi Kurosawa, Yoshiaki Mikami, and Haruki Nakamura · 2014
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What is high-throughput virtual screening? a perspective from organic materials discovery
Edward O. Pyzer-Knapp, Changwon Suh, Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, and Alán Aspuru-Guzik · 2015
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ZINC 15 – ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
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Sequence level training with recurrent neural networks
Marc’Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba · 2015
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Improving multi-step prediction of learned time series models
Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell · 2015
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Scheduled sampling for sequence prediction with recurrent neural networks
Samy Bengio, Oriol Vinyals, Navdeep Jaitly, and Noam Shazeer · 2015
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How (not) to Train your Generative Model: Scheduled Sampling, Likelihood, Adversary?
F. Huszár · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, and Demis Hassabis · 2015
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Innovation in the pharmaceutical industry: New estimates of R&D costs
Joseph A. DiMasi, Henry G. Grabowski, and Ronald W. Hansen · 2016
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Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia, Katherine C. Elbert, Philip D. F. Adler, Casey Falk, Malia B. Wenny, Aurelio Mollo, Matthias Zeller, Sorelle A. Friedler, Joshua Schrier, and Alexander J. Norquist · 2016
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Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
Rafael Gómez-Bombarelli, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, David Duvenaud, Dougal Maclaurin, Martin A. Blood-Forsythe, Hyun Sik Chae, Markus Einzinger, Dong-Gwang Ha, Tony Wu, Georgios Markopoulos, Soonok Jeon, Hosuk Kang, Hiroshi Miyazaki, Masaki Numata, Sunghan Kim, Wenliang Huang, Seong Ik Hong, Marc Baldo, Ryan P. Adams, and Alán Aspuru-Guzik · 2016
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Categorical Reparameterization with Gumbel-Softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
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The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology
Artur Kadurin, Alexander Aliper, Andrey Kazennov, Polina Mamoshina, Quentin Vanhaelen, Kuzma Khrabrov, and Alex Zhavoronkov · 2016
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The harvard organic photovoltaic dataset
Steven A. Lopez, Edward O. Pyzer-Knapp, Gregor N. Simm, Trevor Lutzow, Kewei Li, Laszlo R. Seress, Johannes Hachmann, and Alán Aspuru-Guzik · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Adversarial autoencoders
Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow · 2016
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Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, and Demis Hassabis · 2016
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Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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A note on the evaluation of generative models
L. Theis, A. van den Oord, and M. Bethge · 2016
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Calculating a few too many new compounds, 2016
Derek Lowe · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Sampling generative networks: Notes on a few effective techniques
Tom White · 2016
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Attention and augmented recurrent neural networks
Chris Olah and Shan Carter · 2016
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Remarks on the evolution of explosives
Axel Homburg · 2017
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton · 2017
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Computational antimicrobial peptide design and evaluation against multidrug-resistant clinical isolates of bacteria
Deepesh Nagarajan, Tushar Nagarajan, Natasha Roy, Omkar Kulkarni, Sathyabaarathi Ravichandran, Madhulika Mishra, Dipshikha Chakravortty, and Nagasuma Chandra · 2017
Cited alongside, same era.
Wavelet scattering regression of quantum chemical energies
Matthew Hirn, Stéphane Mallat, and Nicolas Poilvert · 2017
Cited alongside, same era.
Solid harmonic wavelet scattering: Predicting quantum molecular energy from invariant descriptors of 3D electronic densities
Michael Eickenberg, Georgios Exarchakis, Matthew Hirn, and Stephane Mallat · 2017
Cited alongside, same era.
SMILES Enumeration as Data Augmentation for Neural Network Modeling of Molecules
E. Jannik Bjerrum · 2017
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
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Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
J. You, B. Liu, R. Ying, V. Pande, and J. Leskovec · 2018
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Optimization of Molecules via Deep Reinforcement Learning
Z. Zhou, S. Kearnes, L. Li, R. N. Zare, and P. Riley · 2018
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Independent Vector Analysis for Data Fusion Prior to Molecular Property Prediction with Machine Learning
Z. Boukouvalas, D. C. Elton, P. W. Chung, and M. D. Fuge · 2018
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Exploring deep recurrent models with reinforcement learning for molecule design
Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, and Nathan Brown · 2018
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Grammar Variational Autoencoder
M. J. Kusner, B. Paige, and J. M. Hernández-Lobato · 2017
Cited alongside, same era.
Chemception: A Deep Neural Network with Minimal Chemistry Knowledge Matches the Performance of Expert-developed QSAR/QSPR Models
G. B. Goh, C. Siegel, A. Vishnu, N. O. Hodas, and N. Baker · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 2017
Cited alongside, same era.
Molecular Generation with Recurrent Neural Networks (RNNs)
E. Jannik Bjerrum and R. Threlfall · 2017
Cited alongside, same era.
Generative recurrent networks for de novo drug design
Anvita Gupta, Alex T. Müller, Berend J. H. Huisman, Jens A. Fuchs, Petra Schneider, and Gisbert Schneider · 2017
Cited alongside, same era.
Molecular de-novo design through deep reinforcement learning
Marcus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen · 2017
Cited alongside, same era.
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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Hunting for organic molecules with artificial intelligence: Molecules optimized for desired excitation energies
Masato Sumita, Xiufeng Yang, Shinsuke Ishihara, Ryo Tamura, and Koji Tsuda · 2018
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De novo design of bioactive small molecules by artificial intelligence
Daniel Merk, Lukas Friedrich, Francesca Grisoni, and Gisbert Schneider · 2018
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Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators
Daniel Merk, Francesca Grisoni, Lukas Friedrich, and Gisbert Schneider · 2018
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Exploring the GDB-13 Chemical Space Using Deep Generative Models
Josep Arús-Pous, Thomas Blaschke, Silas Ulander, Jean-Louis Reymond, Hongming Chen, and Ola Engkvist · 2018
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De novo molecule design by translating from reduced graphs to SMILES
Peter Pogány, Navot Arad, Sam Genway, and Stephen D. Pickett · 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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Conditional molecular design with deep generative models
Seokho Kang and Kyunghyun Cho · 2018
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Prototype-based compound discovery using deep generative models
Shahar Harel and Kira Radinsky · 2018
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Deep generative models for molecular science
Peter B. Jørgensen, Mikkel N. Schmidt, and Ole Winther · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola · 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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Molecular Hypergraph Grammar with its Application to Molecular Optimization
H. Kajino · 2018
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Learning Continuous and Data-Driven Molecular Descriptors by Translating Equivalent Chemical Representations
Robin Winter, Floriane Montanari, Frank Noé, and Djork-Arné Clevert · 2018
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Designing random graph models using variational autoencoders with applications to chemical design
Bidisha Samanta, Abir De, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2018
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NeVAE: A Deep Generative Model for Molecular Graphs
Bidisha Samanta, Abir De, Gourhari Jana, Pratim Kumar Chattaraj, Niloy Ganguly, and Manuel Gomez-Rodriguez · 2018
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Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
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Reinforced adversarial neural computer for de novo molecular design
Evgeny Putin, Arip Asadulaev, Yan Ivanenkov, Vladimir Aladinskiy, Benjamin Sanchez-Lengeling, Alán Aspuru-Guzik, and Alex Zhavoronkov · 2018
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Adversarial threshold neural computer for molecular de novo design
Evgeny Putin, Arip Asadulaev, Quentin Vanhaelen, Yan Ivanenkov, Anastasia V. Aladinskaya, Alex Aliper, and Alex Zhavoronkov · 2018
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De novo generation of hit-like molecules from gene expression signatures using artificial intelligence
Oscar Méndez-Lucio, Benoit Baillif, Djork-Arné Clevert, David Rouquié, and Joerg Wichard · 2018
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Adversarial autoencoders with constant-curvature latent manifolds
Daniele Grattarola, Lorenzo Livi, and Cesare Alippi · 2018
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Entangled conditional adversarial autoencoder for de novo drug discovery
Daniil Polykovskiy, Alexander Zhebrak, Dmitry Vetrov, Yan Ivanenkov, Vladimir Aladinskiy, Polina Mamoshina, Marine Bozdaganyan, Alexander Aliper, Alex Zhavoronkov, and Artur Kadurin · 2018
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A high-bias, low-variance introduction to Machine Learning for physicists
P. Mehta, M. Bukov, C.-H. Wang, A. G. R. Day, C. Richardson, C. K. Fisher, and D. J. Schwab · 2018
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The GAN Landscape: Losses, Architectures, Regularization, and Normalization
Karol Kurach, Mario Lucic, Xiaohua Zhai, Marcin Michalski, and Sylvain Gelly · 2018
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Winner’s curse? On pace, progress, and empirical rigor
D. Sculley, Jasper Snoek, Alex Wiltschko, and Ali Rahimi · 2018
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Deep reinforcement learning that matters
Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 2018
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Fréchet 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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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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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, Artur Kadurin, Sergey Nikolenko, Alan Aspuru-Guzik, and Alex Zhavoronkov · 2018
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DiversityNet: a collaborative benchmark for generative ai models in chemistry
Mostapha Benhenda, Esben Jannik Bjerrum, Hsiao Yi, and Chintan Zaveri · 2018
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Quantitatively evaluating GANs with divergences proposed for training
Daniel Jiwoong Im, Alllan He Ma, Graham W. Taylor, and Kristin Branson · 2018
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Population-based de novo molecule generation, using grammatical evolution
N. Yoshikawa, K. Terayama, T. Honma, K. Oono, and K. Tsuda · 2018
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Recent applications of machine learning in medicinal chemistry
Jane Panteleev, Hua Gao, and Lei Jia · 2018
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Development of ICA and IVA Algorithms with Application to Medical Image Analysis
Zois Boukouvalas · 2018
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Generative modeling for protein structures
Namrata Anand and Possu Huang · 2018
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CrystalGAN: Learning to Discover Crystallographic Structures with Generative Adversarial Networks
A. Nouira, N. Sokolovska, and J.-C. Crivello · 2018
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A deep adversarial learning methodology for designing microstructural material systems
Xiaolin Li, Zijiang Yang, L. Catherine Brinson, Alok Choudhary, Ankit Agrawal, and Wei Chen · 2018
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Physics-aware Deep Generative Models for Creating Synthetic Microstructures
Rahul Singh, Viraj Shah, Balaji Pokuri, Soumik Sarkar, Baskar Ganapathysubramanian, and Chinmay Hegde · 2018
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Microstructural Materials Design via Deep Adversarial Learning Methodology
Z. Yang, X. Li, L. C. Brinson, A. N. Choudhary, W. Chen, and A. Agrawal · 2018
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Synthesizing designs with inter-part dependencies using hierarchical generative adversarial networks
Ashwin Jeyaseelan Chen, Wei and Mark D. Fuge · 2018
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Building and deploying a cyberinfrastructure for the data-driven design of chemical systems and the exploration of chemical space
Johannes Hachmann, Mohammad Atif Faiz Afzal, Mojtaba Haghighatlari, and Yudhajit Pal · 2018
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Closed-loop discovery platform integration is needed for artificial intelligence to make an impact in drug discovery
Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Jill S. Becker, and Aspuru-Guzik A · 2018
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Accelerating the discovery of materials for clean energy in the era of smart automation
Daniel P. Tabor, Loïc M. Roch, Semion K. Saikin, Christoph Kreisbeck, Dennis Sheberla, Joseph H. Montoya, Shyam Dwaraknath, Muratahan Aykol, Carlos Ortiz, Hermann Tribukait, Carlos Amador-Bedolla, Christoph J. Brabec, Benji Maruyama, Kristin A. Persson, and Alán Aspuru-Guzik · 2018
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Shape-based generative modeling for de-novo drug design
Miha Skalic, José Jiménez Luna, Davide Sabbadin, and Gianni De Fabritiis · 2019
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
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QBMG: quasi-biogenic molecule generator with deep recurrent neural network
Shuangjia Zheng, Xin Yan, Qiong Gu, Yuedong Yang, Yunfei Du, Yutong Lu, and Jun Xu · 2019
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De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping
Boris Sattarov, Igor I. Baskin, Dragos Horvath, Gilles Marcou, Esben Jannik Bjerrum, and Alexandre Varnek · 2019
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Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola · 2019
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Decoding molecular graph embeddings with reinforcement learning
Steven M. Kearnes, Li Li, and Patrick Riley · 2019
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Mol-cyclegan - a generative model for molecular optimization, 2019
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Deep Reinforcement Learning for Multiparameter Optimization in de novo Drug Design
Niclas Ståhl, Goran Falkman, Alexander Karlsson, Gunnar Mathiason, and Jonas Bostrom · 2019
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The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling
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GuacaMol: Benchmarking models for de novo molecular design
Nathan Brown, Marco Fiscato, Marwin H.S. Segler, and Alain C. Vaucher · 2019
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Towards GAN benchmarks which require generalization
Ishaan Gulrajani, Colin Raffel, and Luke Metz · 2019
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A universal density matrix functional from molecular orbital-based machine learning: Transferability across organic molecules
Lixue Cheng, Matthew Welborn, Anders S Christensen, and Thomas F Miller III · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jan H. Jensen · 2019
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Unified rational protein engineering with sequence-only deep representation learning
Ethan C. Alley, Grigory Khimulya, Surojit Biswas, Mohammed AlQuraishi, and George M. Church · 2019
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How to explore chemical space using algorithms and automation
Piotr S. Gromski, Alon B. Henson, Jarosław M. Granda, and Leroy Cronin · 2019
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