Fetching the paper…
Reading the bibliography…
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.
Classification and regression trees
Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen · 1984
Earlier work this paper cites.
Concepts and applications of molecular similarity
Mark A Johnson and Gerald M Maggiora · 1990
Earlier work this paper cites.
Quantum-chemical descriptors in qsar/qspr studies
Mati Karelson, Victor S Lobanov, and Alan R Katritzky · 1996
Earlier work this paper cites.
Daylight chemical information systems
Daylight Toolkit · 1997
Earlier work this paper cites.
Clustering of large databases of compounds: Using the mdl “keys” as structural descriptors
Malcolm J McGregor and Peter V Pallai · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
Reoptimization of mdl keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse · 2002
Earlier work this paper cites.
Comparative study of qsar/qspr correlations using support vector machines, radial basis function neural networks, and multiple linear regression
XJ Yao, Annick Panaye, Jean-Pierre Doucet, RS Zhang, HF Chen, MC Liu, ZD Hu, and Bo Tao Fan · 2004
Earlier work this paper cites.
Mdl information systems inc
MACCS Structural Keys · 2005
Earlier work this paper cites.
Similarity-based virtual screening using 2d fingerprints
Peter Willett · 2006
Earlier work this paper cites.
Design rules for donors in bulk-heterojunction solar cells—towards 10% energy-conversion efficiency
Markus C Scharber, David Mühlbacher, Markus Koppe, Patrick Denk, Christoph Waldauf, Alan J Heeger, and Christoph J Brabec · 2006
Earlier work this paper cites.
Rdkit: Open-source cheminformatics
Greg Landrum · 2006
Earlier work this paper cites.
Topographical and morphological aspects of spray coated organic photovoltaics
Claudia N Hoth, Roland Steim, Pavel Schilinsky, Stelios A Choulis, Sandro F Tedde, Oliver Hayden, and Christoph J Brabec · 2009
Earlier work this paper cites.
Interpretation of qsar models based on random forest methods
Victor E Kuz’min, Pavel G Polishchuk, Anatoly G Artemenko, and Sergey A Andronati · 2011
Earlier work this paper cites.
Multiple input-single output (miso) feedforward artificial neural network (fann) models for pilot plant binary distillation column
Z Abdullah, Zainal Ahmad, and N Aziz · 2011
Earlier work this paper cites.
Representation of chemical structures
Wendy A Warr · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Earlier work this paper cites.
Theoretical study of two-photon absorption properties and up-conversion efficiency of new symmetric organic π \pi -conjugated molecules for photovoltaic devices
Zhong Hu, Vedbar S Khadka, Wei Wang, David W Galipeau, and Xingzhong Yan · 2012
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
Cited alongside, same era.
Efficient estimation of word representations in vector space
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean · 2013
Cited alongside, same era.
Dft and tddft study on the electronic structure and photoelectrochemical properties of dyes derived from cochineal and lac insects as photosensitizer for dye-sensitized solar cells
Wichien Sang-aroon, Seksan Laopha, Phrompak Chaiamornnugool, Sarawut Tontapha, Samarn Saekow, and Vittaya Amornkitbamrung · 2013
Cited alongside, same era.
Combinatorial screening for new materials in unconstrained composition space with machine learning
Bryce Meredig, Ankit Agrawal, Scott Kirklin, James E Saal, JW Doak, A Thompson, Kunpeng Zhang, Alok Choudhary, and Christopher Wolverton · 2014
Cited alongside, same era.
Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al · 2016
Later among the works it cites.
Improvement of photovoltaic performance by substituent effect of donor and acceptor structure of tpa-based dye-sensitized solar cells
Natalia Inostroza, Fernando Mendizabal, Ramiro Arratia-Pérez, Carlos Orellana, and Cristian Linares-Flores · 2016
Later among the works it cites.
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
Later among the works it cites.
Bag of tricks for efficient text classification
Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov · 2016
Later among the works it cites.
Hierarchical attention networks for document classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ankit Agrawal, Parijat D Deshpande, Ahmet Cecen, Gautham P Basavarsu, Alok N Choudhary, and Surya R Kalidindi · 2014
Cited alongside, same era.
Imagenet classification with deep convolutional neural
Alex Krizhevsky, I Sutskever, and G Hinton · 2014
Cited alongside, same era.
Data science and cyberinfrastructure: critical enablers for accelerated development of hierarchical materials
Surya R Kalidindi · 2015
Cited alongside, same era.
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
Cited alongside, same era.
Deep neural nets as a method for quantitative structure–activity relationships
Junshui Ma, Robert P Sheridan, Andy Liaw, George E Dahl, and Vladimir Svetnik · 2015
Cited alongside, same era.
Learning from the harvard clean energy project: The use of neural networks to accelerate materials discovery
Edward O Pyzer-Knapp, Kewei Li, and Alan Aspuru-Guzik · 2015
Cited alongside, same era.
Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
Cited alongside, same era.
Improving event-based rainfall-runoff simulation using an ensemble artificial neural network based hybrid data-driven model
Guangyuan Kan, Cheng Yao, Qiaoling Li, Zhijia Li, Zhongbo Yu, Zhiyu Liu, Liuqian Ding, Xiaoyan He, and Ke Liang · 2015
Cited alongside, same era.
Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy · 2016
Later among the works it cites.
Improved scaling of molecular network calculations: the emergence of molecular domains
Adam G Gagorik, Brett Savoie, Nick Jackson, Ankit Agrawal, Alok Choudhary, Mark A Ratner, George C Schatz, and Kevin L Kohlstedt · 2017
Later among the works it cites.
Garrett B Goh, Charles Siegel, Abhinav Vishnu, Nathan O Hodas, and Nathan Baker · 2017
Later among the works it cites.
Smiles2vec: An interpretable general-purpose deep neural network for predicting chemical properties
Garrett B Goh, Nathan O Hodas, Charles Siegel, and Abhinav Vishnu · 2017
Later among the works it cites.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Data-driven prediction of the high-dimensional thermal history in directed energy deposition processes via recurrent neural networks
Mojtaba Mozaffar, Arindam Paul, Reda Al-Bahrani, Sarah Wolff, Alok Choudhary, Ankit Agrawal, Kornel Ehmann, and Jian Cao · 2018
Closest in time.
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
Closest in time.
https://github.com/GLambard/Molecules_Dataset_Collection
Glambard/molecules_dataset_collection: Collection of data sets of molecules for a validation of properties inference · 2018
Closest in time.
https://github.com/paularindam/CheMixNet
Official repository for the chemixnet library · 2018
Closest in time.
Smiles2vec: Predicting chemical properties from text representations
Garrett B Goh, Nathan Hodas, Charles Siegel, and Abhinav Vishnu · 2018
Closest in time.
https://www.dropbox.com/s/3kqzt9u1ryflls0/CEPData.csv.zip?dl=0
Cepdata.csv.zip · 2018
Closest in time.
https://github.com/maxpumperla/hyperas
maxpumperla/hyperas: Keras + hyperopt: A very simple wrapper for convenient hyperparameter optimization · 2018
Closest in time.
https://github.com/hyperopt/hyperopt
hyperopt/hyperopt: Distributed asynchronous hyperparameter optimization in python · 2018
Closest in time.