Inverse design of 3d molecular structures with conditional generative neural networks
Original
Gebauer, N. W., Gastegger, M., Hessmann, S. S., Müller, K.-R., and Schütt, K. T · 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
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Highly accurate protein structure prediction with alphafold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al · 2021
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Variational diffusion models
Original
Kingma, D. P., Salimans, T., Poole, B., and Ho, J · 2021
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Gg-gan: A geometric graph generative adversarial network, 2021
Krawczuk, I., Abranches, P., Loukas, A., and Cevher, V · 2021
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An autoregressive flow model for 3d molecular geometry generation from scratch
Luo, Y. and Ji, S · 2021
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A graph vae and graph transformer approach to generating molecular graphs
Original
Mitton, J., Senn, H. M., Wynne, K., and Murray-Smith, R · 2021
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Improved denoising diffusion probabilistic models
Original
Nichol, A. and Dhariwal, 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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Symmetry-aware actor-critic for 3d molecular design
Simm, G. N. C., Pinsler, R., Csányi, G., and Hernández-Lobato, J. M · 2021
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Top-n: Equivariant set and graph generation without exchangeability
Original
Vignac, C. and Frossard, P · 2021
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Energy-inspired molecular conformation optimization
Guan, J., Qian, W. W., qiang liu, Ma, W.-Y., Ma, J., and Peng, J · 2022
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Progressive distillation for fast sampling of diffusion models
Original
Salimans, T. and Ho, J · 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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