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The conformational landscape of proteins is crucial to understanding their functionality in complex biological processes.
The protein data bank
Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G., Bhat, T. N., Weissig, H., Shindyalov, I. N., and Bourne, P. E · 2000
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Scoring function for automated assessment of protein structure template quality
Zhang, Y. and Skolnick, J · 2004
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Atomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., and Wriggers, W · 2010
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Slow dynamics of a protein backbone in molecular dynamics simulation revealed by time-structure based independent component analysis
Naritomi, Y. and Fuchigami, S · 2013
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Improved generalized born solvent model parameters for protein simulations
Nguyen, H., Roe, D. R., and Simmerling, C · 2013
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Identification of slow molecular order parameters for markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., and Noé, F · 2013
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ff14sb: Improving the accuracy of protein side chain and backbone parameters from ff99sb
Maier, J. A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K. E., and Simmerling, C · 2015
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PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez-Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., and Noé, F · 2015
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Accurate and reliable prediction of relative ligand binding potency in prospective drug discovery by way of a modern free-energy calculation protocol and force field
Wang, L., Wu, Y., Deng, Y., Kim, B., Pierce, L., Krilov, G., Lupyan, D., Robinson, S., Dahlgren, M. K., Greenwood, J., et al · 2015
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Efficient estimation of rare-event kinetics
Trendelkamp-Schroer, B. and Noé, F · 2016
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Openmm 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al · 2017
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., and Wu, H · 2019
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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FASPR: an open-source tool for fast and accurate protein side-chain packing
Huang, X., Pearce, R., and Zhang, Y · 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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Accurate prediction of protein structures and interactions using a three-track neural network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al · 2021
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Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
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Deeptime: a python library for machine learning dynamical models from time series data
Hoffmann, M., Scherer, M. K., Hempel, T., Mardt, A., de Silva, B., Husic, B. E., Klus, S., Wu, H., Kutz, J. N., Brunton, S., and Noé, F · 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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How fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., and Shaw, D. E · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
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Anton 3: twenty microseconds of molecular dynamics simulation before lunch
Shaw, D. E., Adams, P. J., Azaria, A., Bank, J. A., Batson, B., Bell, A., Bergdorf, M., Bhatt, J., Butts, J. A., Correia, T., et al · 2021
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Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Trippe, B. L., Yim, J., Tischer, D., Baker, D., Broderick, T., Barzilay, R., and Jaakkola, T · 2022
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From sequence to boltzmann weighted ensemble of structures with alphafold2-rave
Vani, B. P., Aranganathan, A., Wang, D., and Tiwary, P · 2022
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Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models
Watson, J. L., Juergens, D., Bennett, N. R., Trippe, B. L., Yim, J., Eisenach, H. E., Ahern, W., Borst, A. J., Ragotte, R. J., Milles, L. F., et al · 2022
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High-resolution de novo structure prediction from primary sequence
Wu, R., Ding, F., Wang, R., Shen, R., Zhang, X., Luo, S., Su, C., Wu, Z., Xie, Q., Berger, B., et al · 2022
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Se (3)-stochastic flow matching for protein backbone generation
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Protein structure and sequence generation with equivariant denoising diffusion probabilistic models
Anand, N. and Achim, T · 2022
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Riemannian score-based generative modelling
De Bortoli, V., Mathieu, E., Hutchinson, M., Thornton, J., Teh, Y. W., and Doucet, A · 2022
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Sampling alternative conformational states of transporters and receptors with alphafold2
Del Alamo, D., Sala, D., Mchaourab, H. S., and Meiler, J · 2022
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Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
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Imagen video: High definition video generation with diffusion models
Ho, J., Chan, W., Saharia, C., Whang, J., Gao, R., Gritsenko, A., Kingma, D. P., Poole, B., Norouzi, M., Fleet, D. J., et al · 2022
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Denoising diffusion probabilistic models on so (3) for rotational alignment
Leach, A., Schmon, S. M., Degiacomi, M. T., and Willcocks, C. G · 2022
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Language models of protein sequences at the scale of evolution enable accurate structure prediction
Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., dos Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., et al · 2022
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Bose, A. J., Akhound-Sadegh, T., Fatras, K., Huguet, G., Rector-Brooks, J., Liu, C.-H., Nica, A. C., Korablyov, M., Bronstein, M., and Tong, A · 2023
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Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al · 2023
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Direct generation of protein conformational ensembles via machine learning
Janson, G., Valdes-Garcia, G., Heo, L., and Feig, M · 2023
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Eigenfold: Generative protein structure prediction with diffusion models
Jing, B., Erives, E., Pao-Huang, P., Corso, G., Berger, B., and Jaakkola, T · 2023
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Lu, C., Chen, H., Chen, J., Su, H., Li, C., and Zhu, J · 2023
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Protein ensemble generation through variational autoencoder latent space sampling
Mansoor, S., Baek, M., Park, H., Lee, G. R., and Baker, D · 2023
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Abdiffuser: Full-atom generation of in-vitro functioning antibodies
Martinkus, K., Ludwiczak, J., Cho, K., Lian, W.-C., Lafrance-Vanasse, J., Hotzel, I., Rajpal, A., Wu, Y., Bonneau, R., Gligorijevic, V., et al · 2023
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Learning interpolations between boltzmann densities
Máté, B. and Fleuret, F · 2023
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Predicting multiple conformations via sequence clustering and alphafold2
Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., and Kern, D · 2023
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Towards predicting equilibrium distributions for molecular systems with deep learning
Zheng, S., He, J., Liu, C., Shi, Y., Lu, Z., Feng, W., Ju, F., Wang, J., Zhu, J., Min, Y., et al · 2023
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Str2str: A score-based framework for zero-shot protein conformation sampling
Lu, J., Zhong, B., Zhang, Z., and Tang, J · 2024
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Energy based diffusion generator for efficient sampling of boltzmann distributions
Wang, Y., Guo, L., Wu, H., and Zhou, T · 2024
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