Fetching the paper…
Reading the bibliography…
We seek to automate the design of molecules based on specific chemical properties.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
Earlier work this paper cites.
Feature trees: a new molecular similarity measure based on tree matching
Rarey, M. and Dixon, J. S · 1998
Earlier work this paper cites.
Organic Chemistry
Clayden, J., Greeves, N., Warren, S., and Wothers, P · 2001
Earlier work this paper cites.
A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
Earlier work this paper cites.
Rdkit: Open-source cheminformatics
Landrum, G · 2006
Earlier work this paper cites.
The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
Earlier work this paper cites.
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and LeCun, Y · 2013
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., and Potts, C · 2013
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
Earlier work this paper cites.
Gated graph sequence neural networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
Cited alongside, same era.
Improved semantic representations from tree-structured long short-term memory networks
Tai, K. S., Socher, R., and Manning, C. D · 2015
Cited alongside, same era.
Grammar as a foreign language
Vinyals, O., Kaiser, Ł., Koo, T., Petrov, S., Sutskever, I., and Hinton, G · 2015
Cited alongside, same era.
Long short-term memory over recursive structures
Zhu, X., Sobihani, P., and Guo, H · 2015
Cited alongside, same era.
Tree-structured decoding with doubly-recurrent neural networks
Alvarez-Melis, D. and Jaakkola, T. S · 2016
Cited alongside, same era.
Discriminative embeddings of latent variable models for structured data
Dai, H., Dai, B., and Song, L · 2016
Neuro-symbolic program synthesis
Parisotto, E., Mohamed, A.-r., Singh, R., Li, L., Zhou, D., and Kohli, P · 2016
Later among the works it cites.
Towards string-to-tree neural machine translation
Aharoni, R. and Goldberg, Y · 2017
Later among the works it cites.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Later among the works it cites.
Objective-reinforced generative adversarial networks (organ) for sequence generation models
Guimaraes, G. L., Sanchez-Lengeling, B., Farias, P. L. C., and Aspuru-Guzik, A · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Language to logical form with neural attention
Dong, L. and Lapata, M · 2016
Cited alongside, same era.
Recurrent neural network grammars
Dyer, C., Kuncoro, A., Ballesteros, M., and Smith, N. A · 2016
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
Cited alongside, same era.
Easy-first dependency parsing with hierarchical tree lstms
Kiperwasser, E. and Goldberg, Y · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
Cited alongside, same era.
Janz, D., van der Westhuizen, J., and Hernández-Lobato, J. M · 2017
Later among the works it cites.
Predicting organic reaction outcomes with weisfeiler-lehman network
Jin, W., Coley, C., Barzilay, R., and Jaakkola, T · 2017
Later among the works it cites.
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
Later among the works it cites.
Deriving neural architectures from sequence and graph kernels
Lei, T., Jin, W., Barzilay, R., and Jaakkola, T · 2017
Later among the works it cites.
Sequence to better sequence: continuous revision of combinatorial structures
Mueller, J., Gifford, D., and Jaakkola, T · 2017
Later among the works it cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Felix, H. E. S., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
Later among the works it cites.
Generating focussed molecule libraries for drug discovery with recurrent neural networks
Segler, M. H., Kogej, T., Tyrchan, C., and Waller, M. P · 2017
Later among the works it cites.
Syntax-directed variational autoencoder for structured data
Dai, H., Tian, Y., Dai, B., Skiena, S., and Song, L · 2018
Closest in time.
Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 2018
Closest in time.
Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N · 2018
Closest in time.