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Deep generative models have recently achieved superior performance in 3D molecule generation.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
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Open babel: An open chemical toolbox
O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vandermeersch, T., and Hutchison, G. R · 2011
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Grammar variational autoencoder
Kusner, M. J., Paige, B., and Hernández-Lobato, J. M · 2017
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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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Constrained graph variational autoencoders for molecule design
Liu, Q., Allamanis, M., Brockschmidt, M., and Gaunt, A · 2018
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Predictive uncertainty estimation via prior networks
Malinin, A. and Gales, M · 2018
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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 · 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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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N., Gastegger, M., and Schütt, K · 2019
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Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
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Optimization of molecules via deep reinforcement learning
Zhou, Z., Kearnes, S., Li, L., Zare, R. N., and Riley, P · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Composing molecules with multiple property constraints
Jin, W., Barzilay, R., and Jaakkola, T · 2020
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Permutation invariant graph generation via score-based generative modeling
Niu, C., Song, Y., Song, J., Zhao, S., Grover, A., and Ermon, S · 2020
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Learning a continuous representation of 3d molecular structures with deep generative models
Ragoza, M., Masuda, T., and Koes, D. R · 2020
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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 · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
Equivariant 3d-conditional diffusion models for molecular linker design
Igashov, I., Stärk, H., Vignac, C., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., and Correia, B · 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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Diffbp: Generative diffusion of 3d molecules for target protein binding
Lin, H., Huang, Y., Liu, M., Li, X., Ji, S., and Li, S. Z · 2022
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Generating 3d molecules for target protein binding
Liu, M., Luo, Y., Uchino, K., Maruhashi, K., and Ji, S · 2022
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An autoregressive flow model for 3d molecular geometry generation from scratch
Luo, Y. and Ji, S · 2022
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Argmax flows and multinomial diffusion: Learning categorical distributions
Hoogeboom, E., Nielsen, D., Jaini, P., Forré, P., and Welling, M · 2021
Cited alongside, same era.
Structure-based de novo drug design using 3d deep generative models
Li, Y., Pei, J., and Lai, L · 2021
Cited alongside, same era.
A 3d generative model for structure-based drug design
Luo, S., Guan, J., Ma, J., and Peng, J · 2021
Cited alongside, same era.
Improved denoising diffusion probabilistic models
Nichol, A. Q. and Dhariwal, P · 2021
Cited alongside, same era.
Geom, energy-annotated molecular conformations for property prediction and molecular generation
Axelrod, S. and Gómez-Bombarelli, R · 2022
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Energy-inspired molecular conformation optimization
Guan, J., Qian, W. W., Liu, Q., Ma, W.-Y., Ma, J., and Peng, J · 2022
Cited alongside, same era.
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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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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Generating realistic 3d molecules with an equivariant conditional likelihood model, 2022
Roney, J. P., Maragakis, P., Skopp, P., and Shaw, D. E · 2022
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Structure-based drug design with equivariant diffusion models
Schneuing, A., Du, Y., Harris, C., Jamasb, A., Igashov, I., Du, W., Blundell, T., Lió, P., Gomes, C., Welling, M., et al · 2022
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Digress: Discrete denoising diffusion for graph generation
Vignac, C., Krawczuk, I., Siraudin, A., Wang, B., Cevher, V., and Frossard, P · 2022
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Diffusion-based molecule generation with informative prior bridges
Wu, L., Gong, C., Liu, X., Ye, M., and qiang liu · 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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3d equivariant diffusion for target-aware molecule generation and affinity prediction
Guan, J., Qian, W. W., Peng, X., Su, Y., Peng, J., and Ma, J · 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
Vignac, C., Osman, N., Toni, L., and Frossard, P · 2023
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