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We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings.
Robust estimation of a location parameter
Huber, P. J · 1964
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Atom pairs as molecular features in structure-activity studies: definition and applications
Carhart, R. E., Smith, D. H., and Venkataraghavan, R · 1985
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SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
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RDKit: Open-source cheminformatics
Landrum, G. et al · 2006
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Extended-connectivity fingerprints
Rogers, D. and Hahn, M · 2010
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ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., and Overington, J. P · 2012
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Do not hesitate to use tversky-and other hints for successful active analogue searches with feature count descriptors
Horvath, D., Marcou, G., and Varnek, A · 2013
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., and Dean, J · 2013
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Learning phrase representations using RNN Encoder-Decoder for statistical machine translation
Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Cited alongside, same era.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A · 2014
Cited alongside, same era.
TensorFlow: Large-scale machine learning on heterogeneous systems, 2015
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D · 2015
Cited alongside, same era.
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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Syntax-Directed variational autoencoder for structured data
Dai, H., Tian, Y., Dai, B., Skiena, S., and Song, L · 2018
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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 · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
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GraphVAE: Towards generation of small graphs using variational autoencoders
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van Hasselt, H., Guez, A., and Silver, D · 2015
Cited alongside, same era.
Tutorial on variational autoencoders
Doersch, C · 2016
Cited alongside, same era.
Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Cited alongside, same era.
Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
Cited alongside, same era.
Time limits in reinforcement learning
Pardo, F., Tavakoli, A., Levdik, V., and Kormushev, P · 2017
Cited alongside, same era.
Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P
Cited in the paper.
Multi-Objective de novo drug design with conditional graph generative model
Li, Y., Zhang, L., and Liu, Z
Cited in the paper.
Simonovsky, M. and Komodakis, N · 2018
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Graph convolutional policy network for Goal-Directed molecular graph generation
You, J., Liu, B., Ying, R., Pande, V., and Leskovec, J · 2018
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Optimization of molecules via deep reinforcement learning
Zhou, Z., Kearnes, S., Li, L., Zare, R. N., and Riley, P · 2018
Later among the works it cites.
Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 2018
Later among the works it cites.