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Automating molecular design using deep reinforcement learning (RL) holds the promise of accelerating the discovery of new chemical compounds.
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
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Prediction of Physicochemical Parameters by Atomic Contributions
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Actor-critic algorithms
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Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements
Stewart, J. J. P · 2007
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Optimization of Molecules via Deep Reinforcement Learning
Zhou, Z., Kearnes, S., Li, L., Zare, R. N., and Riley, P · 2007
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Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17
Ruddigkeit, L., van Deursen, R., Blum, L. C., and Reymond, J.-L · 2012
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Estimation of the size of drug-like chemical space based on GDB-17 data
Polishchuk, P. G., Madzhidov, T. I., and Varnek, A · 2013
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Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A · 2014
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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
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P · 2015
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Reinforcement learning with parameterized actions
Masson, W., Ranchod, P., and Konidaris, G · 2016
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Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
Jin, W., Coley, C., Barzilay, R., and Jaakkola, T · 2017
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Grammar Variational Autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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Molecular de-novo design through deep reinforcement learning
Olivecrona, M., Blaschke, T., Engkvist, O., and Chen, H · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O · 2017
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SchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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Application of Generative Autoencoder in De Novo Molecular Design
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., and Chen, H · 2018
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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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MolGAN: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, T · 2018
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Deep reinforcement learning for de novo drug design
Popova, M., Isayev, O., and Tropsha, A · 2018
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Reinforced Adversarial Neural Computer for de Novo Molecular Design
Putin, E., Asadulaev, A., Ivanenkov, Y., Aladinskiy, V., Sanchez-Lengeling, B., Aspuru-Guzik, A., and Zhavoronkov, A · 2018
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Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., and Waller, M. P · 2018
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Hierarchical approaches for reinforcement learning in parameterized action space
Wei, E., Wicke, D., and Luke, S · 2018
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Xiong, J., Wang, Q., Yang, Z., Sun, P., Han, L., Zheng, Y., Fu, H., Zhang, T., Liu, J., and Liu, H · 2018
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Gebauer, N. W. A., Gastegger, M., and Schütt, K. T · 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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Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models
Guimaraes, G. L., Sanchez-Lengeling, B., Outeiral, C., Farias, P. L. C., and Aspuru-Guzik, A · 2018
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Comprehensive Analysis of the Neglect of Diatomic Differential Overlap Approximation
Husch, T. and Reiher, M · 2018
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Semiempirical molecular orbital models based on the neglect of diatomic differential overlap approximation
Husch, T., Vaucher, A. C., and Reiher, M · 2018
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Molecular generative model based on conditional variational autoencoder for de novo molecular design
Lim, J., Ryu, S., Kim, J. W., and Kim, W. Y · 2018
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Constrained Graph Variational Autoencoders for Molecule Design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A · 2018
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Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
You, J., Liu, B., Ying, Z., Pande, V., and Leskovec, J · 2018
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qcscine/sparrow: Release 1.0.0, 2019
Bosia, F., Husch, T., Vaucher, A. C., and Reiher, M · 2019
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GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N. W. A., Gastegger, M., and Schütt, K. T · 2019
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Atomistic structure learning
Jørgensen, M. S., Mortensen, H. L., Meldgaard, S. A., Kolsbjerg, E. L., Jacobsen, T. L., Sørensen, K. H., and Hammer, B · 2019
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RDKit 2019.09.3
Landrum, G · 2019
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Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., Zentgraf, M., Hill, J. E., Krutoholow, E., Kohler, M., Blaney, J., Funatsu, K., Luebkemann, C., and Schneider, G · 2019
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., Volkov, Y., Zholus, A., Shayakhmetov, R. R., Zhebrak, A., Minaeva, L. I., Zagribelnyy, B. A., Lee, L. H., Soll, R., Madge, D., Xing, L., Guo, T., and Aspuru-Guzik, A · 2019
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