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Generating molecules, both in a directed and undirected fashion, is a huge part of the drug discovery pipeline.
Graphnvp: An invertible flow model for generating molecular graphs
Madhawa, K., Ishiguro, K., Nakago, K., and Abe, M. (2019) · 1905
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Molecularrnn: Generating realistic molecular graphs with optimized properties
Popova, M., Shvets, M., Oliva, J., and Isayev, O. (2019) · 1905
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Genetic algorithms
Holland, J. H. (1992) · 1992
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Genetic algorithms: A survey
Srinivas, M. and Patnaik, L. M. (1994) · 1994
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Moflow: an invertible flow model for generating molecular graphs
Zang, C. and Wang, F. (2020) · 1994
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J. (2015) · 2015
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T. (2018) · 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) · 2018
Cited alongside, same era.
Guacamol: benchmarking models for de novo molecular design
Brown, N., Fiscato, M., Segler, M. H., and Vaucher, A. C. (2019) · 2019
Cited alongside, same era.
A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jensen, J. H. (2019) · 2019
Cited alongside, same era.
On failure modes in molecule generation and optimization
Renz, P., Van Rompaey, D., Wegner, J. K., Hochreiter, S., and Klambauer, G. (2019) · 2019
Cited alongside, same era.
Categorical normalizing flows via continuous transformations
Lippe, P. and Gavves, E. (2020) · 2020
Cited alongside, same era.
Molecular sets (moses): a benchmarking platform for molecular generation models
Graphebm: Molecular graph generation with energy-based models
Liu, M., Yan, K., Oztekin, B., and Ji, S. (2021) · 2021
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Graphdf: A discrete flow model for molecular graph generation
Luo, Y., Yan, K., and Ji, S. (2021) · 2021
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Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (stoned) algorithm for molecules using selfies
Nigam, A., Pollice, R., Krenn, M., dos Passos Gomes, G., and Aspuru-Guzik, A. (2021) · 2021
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Molgensurvey: A systematic survey in machine learning models for molecule design
Du, Y., Fu, T., Sun, J., and Liu, S. (2022) · 2022
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Sample efficiency matters: a benchmark for practical molecular optimization
Gao, W., Fu, T., Sun, J., and Coley, C. (2022) · 2022
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Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · 2020
Cited alongside, same era.
Graphaf: a flow-based autoregressive model for molecular graph generation
Shi*, C., Xu*, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J. (2020) · 2020
Cited alongside, same era.
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Modular flows: Differential molecular generation
Verma, Y., Kaski, S., Heinonen, M., and Garg, V. (2022) · 2022
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