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A well-known limitation of existing molecular generative models is that the generated molecules highly resemble those in the training set.
Consideration of molecular weight during compound selection in virtual target-based database screening
Pan, Y., Huang, N., Cho, S., and Jr., A. D. M · 2003
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Fast neighborhood subgraph pairwise distance kernel
Costa, F. and De Grave, K · 2010
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Zinc: a free tool to discover chemistry for biology
Irwin, J. J., Sterling, T., Mysinger, M. M., Bolstad, E. S., and Coleman, R. G · 2012
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Usrcat: real-time ultrafast shape recognition with pharmacophoric constraints
Schreyer, A. M. and Blundell, T · 2012
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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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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Fast, accurate, and reliable molecular docking with quickvina 2
Alhossary, A., Handoko, S. D., Mu, Y., and Kwoh, C.-K · 2015
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2015
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Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R., Duvenaud, D., Hernández-Lobato, J. M., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., and Aspuru-Guzik, A · 2016
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Rdkit: Open-source cheminformatics software, 2016
Landrum, G. et al · 2016
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Lima Guimaraes, G., Sanchez-Lengeling, B., Outeiral, C., Cunha Farias, P. L., and Aspuru-Guzik, A · 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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Molgan: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, 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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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T · 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. L · 2018
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UMAP: uniform manifold approximation and projection for dimension reduction
McInnes, L. and Healy, J · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., and Klambauer, G · 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
Cited alongside, same era.
Implicit generation and modeling with energy based models
Du, Y. and Mordatch, I · 2019
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A graph-based genetic algorithm and generative model/monte carlo tree search for the exploration of chemical space
Jensen, J. H · 2019
Cited alongside, same era.
Molecularrnn: Generating realistic molecular graphs with optimized properties
Popova, M., Shvets, M., Oliva, J., and Isayev, O · 2019
Cited alongside, same era.
Generative models for automatic chemical design
Schwalbe-Koda, D. and Gómez-Bombarelli, R · 2019
Cited alongside, same era.
Moflow: an invertible flow model for generating molecular graphs
Zang, C. and Wang, F · 2020
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Flow network based generative models for non-iterative diverse candidate generation
Bengio, E., Jain, M., Korablyov, M., Precup, D., and Bengio, Y · 2021
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ILVR: conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
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Score-based diffusion models for accelerated MRI
Chung, H. and Ye, J. C · 2021
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Robust compressed sensing MRI with deep generative priors
Jalal, A., Arvinte, M., Daras, G., Price, E., Dimakis, A. G., and Tamir, J. I · 2021
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Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
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., et al · 2019
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning
Zhou, Z., Kearnes, S., Li, L., Zare, R. N., and Riley, P · 2019
Cited alongside, same era.
Guiding deep molecular optimization with genetic exploration
Ahn, S., Kim, J., Lee, H., and Shin, J · 2020
Cited alongside, same era.
Memory-assisted reinforcement learning for diverse molecular de novo design
Blaschke, T., Engkvist, O., Bajorath, J., and Chen, H · 2020
Cited alongside, same era.
We should at least be able to design molecules that dock well
Cieplinski, T., Danel, T., Podlewska, S., and Jastrzebski, S · 2020
Cited alongside, same era.
Graphebm: Molecular graph generation with energy-based models
Liu, M., Yan, K., Oztekin, B., and Ji, S · 2021
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Sdedit: Image synthesis and editing with stochastic differential equations
Meng, C., Song, Y., Song, J., Wu, J., Zhu, J., and Ermon, S · 2021
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Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2021
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UNIT-DDPM: unpaired image translation with denoising diffusion probabilistic models
Sasaki, H., Willcocks, C. G., and Breckon, T. P · 2021
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Learning gradient fields for molecular conformation generation
Shi, C., Luo, S., Xu, M., and Tang, J · 2021
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Hit and lead discovery with explorative RL and fragment-based molecule generation
Yang, S., Hwang, D., Lee, S., Ryu, S., and Hwang, S. J · 2021
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Limo: Latent inceptionism for targeted molecule generation
Eckmann, P., Sun, K., Zhao, B., Feng, M., Gilson, M. K., and Yu, R · 2022
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Sample efficiency matters: a benchmark for practical molecular optimization
Gao, W., Fu, T., Sun, J., and Coley, C · 2022
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Dockstring: easy molecular docking yields better benchmarks for ligand design
García-Ortegón, M., Simm, G. N., Tripp, A. J., Hernández-Lobato, J. M., Bender, A., and Bacallado, S · 2022
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Equivariant diffusion for molecule generation in 3d
Hoogeboom, E., Satorras, V. G., Vignac, C., and Welling, M · 2022
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., Lee, S., and Hwang, S. J · 2022
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Srdiff: Single image super-resolution with diffusion probabilistic models
Li, H., Yang, Y., Chang, M., Chen, S., Feng, H., Xu, Z., Li, Q., and Chen, Y · 2022
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Pocket2mol: Efficient molecular sampling based on 3d protein pockets
Peng, X., Luo, S., Guan, J., Xie, Q., Peng, J., and Ma, J · 2022
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Generating high fidelity data from low-density regions using diffusion models
Sehwag, V., Hazirbas, C., Gordo, A., Ozgenel, F., and Canton-Ferrer, C · 2022
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Geodiff: a geometric diffusion model for molecular conformation generation
Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J · 2022
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How much space has been explored? measuring the chemical space covered by databases and machine-generated molecules
Xie, Y., Xu, Z., Ma, J., and Mei, Q · 2023
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