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Have you ever been troubled by the complexity and computational cost of SE(3) protein structure modeling and been amazed by the simplicity and power of language modeling? Recent work has shown promise in simplifying protein structures as sequences of protein angles; therefore, language models could be used for unconstrained protein backbone generation.
The dominant role of side-chain backbone interactions in structural realization of amino acid code. chirotor: A side-chain prediction algorithm based on side-chain backbone interactions
Spassov, V. Z., Yan, L., and Flook, P. K · 2007
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Charge interactions can dominate the dimensions of intrinsically disordered proteins
Müller-Späth, S., Soranno, A., Hirschfeld, V., Hofmann, H., Rüegger, S., Reymond, L., Nettels, D., and Schuler, B · 2010
Earlier work this paper cites.
Direct prediction of profiles of sequences compatible with a protein structure by neural networks with fragment-based local and energy-based nonlocal profiles
Li, Z., Yang, Y., Faraggi, E., Zhan, J., and Zhou, Y · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Generative modeling for protein structures
Anand, N. and Huang, P · 2018
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Spin2: Predicting sequence profiles from protein structures using deep neural networks
O’Connell, J., Li, Z., Hanson, J., Heffernan, R., Lyons, J., Paliwal, K., Dehzangi, A., Yang, Y., and Zhou, Y · 2018
Earlier work this paper cites.
Computational protein design with deep learning neural networks
Wang, J., Cao, H., Zhang, J. Z., and Qi, Y · 2018
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To improve protein sequence profile prediction through image captioning on pairwise residue distance map
Chen, S., Sun, Z., Lin, L., Liu, Z., Liu, X., Chong, Y., Lu, Y., Zhao, H., and Yang, Y · 2019
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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
Earlier work this paper cites.
Generative models for graph-based protein design
Ingraham, J., Garg, V. K., Barzilay, R., and Jaakkola, T · 2019
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Molecular geometry prediction using a deep generative graph neural network
Mansimov, E., Mahmood, O., Kang, S., and Cho, K · 2019
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Wavegrad: Estimating gradients for waveform generation
Chen, N., Zhang, Y., Zen, H., Weiss, R. J., Norouzi, M., and Chan, W · 2020
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Physical chemistry of the protein backbone: Enabling the mechanisms of intrinsic protein disorder
Drake, J. A. and Pettitt, B. M · 2020
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Deep learning in protein structural modeling and design
Gao, W., Mahajan, S. P., Sulam, J., and Gray, J. J · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Earlier work this paper cites.
Deep generative models for 3d linker design
Imrie, F., Bradley, A. R., van der Schaar, M., and Deane, C. M · 2020
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Learning from protein structure with geometric vector perceptrons
Jing, B., Eismann, S., Suriana, P., Townshend, R. J., and Dror, R · 2020
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Diffwave: A versatile diffusion model for audio synthesis
Kong, Z., Ping, W., Huang, J., Zhao, K., and Catanzaro, B · 2020
Earlier work this paper cites.
3dmolnet: a generative network for molecular structures
Nesterov, V., Wieser, M., and Roth, V · 2020
Earlier work this paper cites.
Densecpd: improving the accuracy of neural-network-based computational protein sequence design with densenet
Qi, Y. and Zhang, J. Z · 2020
Earlier work this paper cites.
Ramanet: Computational de novo helical protein backbone design using a long short-term memory generative neural network
Sabban, S. and Markovsky, M · 2020
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Earlier work this paper cites.
Fast and flexible protein design using deep graph neural networks
Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., and Kim, P. M · 2020
Cited alongside, same era.
Design of proteins presenting discontinuous functional sites using deep learning
Tischer, D., Lisanza, S., Wang, J., Dong, R., Anishchenko, I., Milles, L. F., Ovchinnikov, S., and Baker, D · 2020
Cited alongside, same era.
Learning neural generative dynamics for molecular conformation generation
Xu, M., Luo, S., Bengio, Y., Peng, J., and Tang, J · 2020
Cited alongside, same era.
Prodconn: Protein design using a convolutional neural network
Zhang, Y., Chen, Y., Wang, C., Lo, C.-C., Liu, X., Wu, W., and Zhang, J · 2020
Cited alongside, same era.
Structured denoising diffusion models in discrete state-spaces
Austin, J., Johnson, D. D., Ho, J., Tarlow, D., and van den Berg, R · 2021
Cited alongside, same era.
Ig-vae: Generative modeling of protein structure by direct 3d coordinate generation
Eguchi, R. R., Choe, C. A., and Huang, P.-S · 2022
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From sequence to function through structure: deep learning for protein design
Ferruz, N., Heinzinger, M., Akdel, M., Goncearenco, A., Naef, L., and Dallago, C · 2022
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Antibody complementarity determining regions (cdrs) design using constrained energy model
Fu, T. and Sun, J · 2022
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Inverse design of 3d molecular structures with conditional generative neural networks
Gebauer, N. W., Gastegger, M., Hessmann, S. S., Müller, K.-R., and Schütt, K. T · 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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Learning inverse folding from millions of predicted structures
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Baranchuk, D., Rubachev, I., Voynov, A., Khrulkov, V., and Babenko, A · 2021
Cited alongside, same era.
Iterative refinement graph neural network for antibody sequence-structure co-design
Jin, W., Wohlwend, J., Barzilay, R., and Jaakkola, T · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Nu-wave: A diffusion probabilistic model for neural audio upsampling
Lee, J. and Han, S · 2021
Cited alongside, same era.
Predicting molecular conformation via dynamic graph score matching
Luo, S., Shi, C., Xu, M., and Tang, J · 2021
Cited alongside, same era.
An autoregressive flow model for 3d molecular geometry generation from scratch
Luo, Y. and Ji, S · 2021
Cited alongside, same era.
Structure-based protein design with deep learning
Ovchinnikov, S. and Huang, P.-S · 2021
Cited alongside, same era.
Hsu, C., Verkuil, R., Liu, J., Lin, Z., Hie, B., Sercu, T., Lerer, A., and Rives, A · 2022
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Torsional diffusion for molecular conformer generation
Jing, B., Corso, G., Chang, J., Barzilay, R., and Jaakkola, T · 2022
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End-to-end deep structure generative model for protein design
Lai, B., Xu, J., et al · 2022
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Proteinsgm: Score-based generative modeling for de novo protein design
Lee, J. S. and Kim, P. M · 2022
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Diffusion-lm improves controllable text generation
Li, X. L., Thickstun, J., Gulrajani, I., Liang, P., and Hashimoto, T. B · 2022
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Repaint: Inpainting using denoising diffusion probabilistic models
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L · 2022
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A 3d molecule generative model for structure-based drug design
Luo, S., Guan, J., Ma, J., and Peng, J · 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 3D molecules conditional on receptor binding sites with deep generative models
Ragoza, M., Masuda, T., and Koes, D. R · 2022
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Deep generative modeling for protein design
Strokach, A. and Kim, P. M · 2022
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Generative de novo protein design with global context
Tan, C., Gao, Z., Xia, J., and Li, S. Z · 2022
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Broderick, T., Baker, D., Barzilay, R., and Jaakkola, T · 2022
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Deblurring via stochastic refinement
Whang, J., Delbracio, M., Talebi, H., Saharia, C., Dimakis, A. G., and Milanfar, P · 2022
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Diffusion models for implicit image segmentation ensembles
Wolleb, J., Sandkühler, R., Bieder, F., Valmaggia, P., and Cattin, P. 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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Direct molecular conformation generation
Zhu, J., Xia, Y., Liu, C., Wu, L., Xie, S., Wang, T., Wang, Y., Zhou, W., Qin, T., Li, H., et al · 2022
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