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A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a.
Computer-assisted design of complex organic syntheses
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Weininger, D · 1988
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The logic of chemical synthesis: multistep synthesis of complex carbogenic molecules (nobel lecture)
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Long short-term memory
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A collection of robust organic synthesis reactions for in silico molecule design
Hartenfeller, M., Eberle, M., Meier, P., Nieto-Oberhuber, C., Altmann, K.-H., Schneider, G., Jacoby, E., and Renner, S · 2011
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Lowe, D · 2012
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Neural machine translation by jointly learning to align and translate
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Auto-encoding variational bayes
Kingma, D. and Welling, M · 2014
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Adam: A method for stochastic optimization
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Scheduled sampling for sequence prediction with recurrent neural networks
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N · 2015
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Convolutional networks on graphs for learning molecular fingerprints
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2016
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Rdkit: Open-source cheminformatics software
Landrum, G · 2016
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Computer-assisted synthetic planning: The end of the beginning
Szymkuć, S., Gajewska, E., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., and Grzybowski, B · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W., Ying, Z., and Leskovec, J · 2017
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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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Retrosynthetic reaction prediction using neural sequence-to-sequence models
Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Nguyen, Q., Ho, S., Sloane, J., Wender, P., and Pande, V · 2017
Machine learning in computer-aided synthesis planning
Coley, C., Green, W., and Jensen, K · 2018
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Learning deep generative models of graphs
Li, Y., Vinyals, O., Dyer, C., Pascanu, R., and Battaglia, P · 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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Exploring deep recurrent models with reinforcement learning for molecule design
Neil, D., Segler, M. H. S., Guasch, L., Ahmed, M., Plumbley, D., Sellwood, M., and Brown, N · 2018
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Designing random graph models using variational autoencoders with applications to chemical design
Samanta, B., De, A., Ganguly, N., and Gomez-Rodriguez, M · 2018
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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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Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
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Quantum-chemical insights from deep tensor neural networks
Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., and Tkatchenko, A · 2017
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”found in translation”: Predicting outcomes of complex organic chemistry reactions using neural sequence-to-sequence models
Schwaller, P., Gaudin, T., Lanyi, D., Bekas, C., and Laino, T · 2017
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Neural-symbolic machine learning for retrosynthesis and reaction prediction
Segler, M. and Waller, M · 2017
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Generating focused molecule libraries for drug discovery with recurrent neural networks
Segler, M. H., Kogej, T., Tyrchan, C., and Waller, M. P · 2017
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., and Welling, M · 2018
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Molecular transformer for chemical reaction prediction and uncertainty estimation
Schwaller, P., Laino, T., Gaudin, T., Bolgar, P., Bekas, C., and Lee, A. A · 2018
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Enhancing retrosynthetic reaction prediction with deep learning using multiscale reaction classification
Baylon, J., Cilfone, N., Gulcher, J., and Chittenden, T · 2019
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Retrosynthesis prediction with conditional graph logic network
Dai, H., Li, C., Coley, C., Dai, B., and Song, L · 2019
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A transformer model for retrosynthesis
Karpov, P., Godin, G., and Tetko, I · 2019
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Popova, M., Shvets, M., Oliva, J., and Isayev, O · 2019
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Graph{af}: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2020
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