Artificial intelligence in the experimental determination and prediction of macromolecular structures

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) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works

Proceedings of IEEE Conference on Computer Vision and Pattern Recognition

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Artificial intelligence: A modern approach

Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,726 · see citing works

Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation

Wrapp, D., Wang, N., Corbett, K. S., Goldsmith, J. A., Hsieh, C.-L., Abiona, O., Graham, B. S., & McLellan, J. S. (2020) · Science · doi:10.1126/science.abb2507 · Cited by 8,897 · see citing works

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759 · see citing works

Accurate prediction of protein structures and interactions using a 3-track 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) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524 · see citing works

Announcing the worldwide Protein Data Bank

Berman, H., Henrick, K., & Nakamura, H. (2003) · Nature Structural & Molecular Biology · doi:10.1038/nsb1203-980 · Cited by 3,093 · see citing works

Nonlinear principal component analysis using autoassociative neural networks

Kramer, M. A. (1991) · AIChE Journal · doi:10.1002/aic.690370209 · Cited by 3,037 · see citing works

Highly accurate protein structure prediction for the human proteome

Tunyasuvunakool, K., Adler, J., Wu, Z., Green, T., Zielinski, M., Žídek, A., Bridgland, A., Cowie, A., Meyer, C., Laydon, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03828-1 · Cited by 3,025 · see citing works

Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

Bepler, T., Morin, A., Rapp, M., Brasch, J., Shapiro, L., Noble, A., & Berger, B. (2018) · Nature Methods · doi:10.1038/s41592-019-0575-8 · Cited by 1,455 · see citing works

Explainable AI: Interpreting, Explaining and Visualizing Deep Learning

(2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-28954-6 · Cited by 1,377 · see citing works

SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM

Wagner, T., Merino, F., Stabrin, M., Moriya, T., Antoni, C., Apelbaum, A., Hagel, P., Sitsel, O., Raisch, T., Prumbaum, D., et al. (2019) · Communications Biology · doi:10.1038/s42003-019-0437-z · Cited by 1,217 · see citing works

Real-time cryo–EM data pre-processing with Warp

Tegunov, D., & Cramer, P. (2018) · Nature Methods · doi:10.1038/s41592-019-0580-y · Cited by 923 · see citing works

How to Explain Individual Classification DecisionsPreprint

Baehrens, D., Schroeter, T. S., Stefan Harmeling, B., Kawanabe, M., Hansen, K., & Mueller, K.-R. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0912.1128 · Cited by 743 · see citing works

Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point cloudsPreprint

Thomas, N., Smidt, T., Kearnes, S. M., Yang, L., Li, L., Kohlhoff, K., & Riley, P. F. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.08219 · Cited by 726 · see citing works

CryoDRGN: reconstruction of heterogeneous cryo-EM structures using neural networks

Zhong, E. D., Bepler, T., Berger, B., & Davis, J. H. (2021) · Nature Methods · doi:10.1038/s41592-020-01049-4 · Cited by 613 · see citing works

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

(2017) · doi:10.1109/bibm41494.2017 · Cited by 327 · see citing works

EMDataBank.org: unified data resource for CryoEM

Lawson, C. L., Baker, M. L., Best, C., Bi, C., Dougherty, M., Feng, P., van Ginkel, G., Devkota, B., Lagerstedt, I. C., Ludtke, S. J., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq880 · Cited by 263 · see citing works

An explainable artificial intelligence system for small-unit tactical behavior

van Lent, M., Fisher, W. M., & Mancuso, M. R. (2004) · Cited by 215 · see citing works

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM

Wang, F. Y., Gong, H., Liu, G., Li, M., Yan, C., Xia, T., Li, X., & Zeng, J. (2016) · Journal of Structural Biology · doi:10.1016/j.jsb.2016.07.006 · Cited by 203 · see citing works

Identification of Secondary Structure Elements in Intermediate-Resolution Density Maps

Baker, M. L., Ju, T., & Chiu, W. K. (2007) · Structure · doi:10.1016/j.str.2006.11.008 · Cited by 202 · see citing works

Deep learning-based mixed-dimensional Gaussian mixture model for characterizing variability in cryo-EM

Chen, M., & Ludtke, S. J. (2021) · Nature Methods · doi:10.1038/s41592-021-01220-5 · Cited by 186 · see citing works

2019 53rd Asilomar Conference on Signals, Systems, and Computers

(2019) · doi:10.1109/ieeeconf44664.2019 · Cited by 130 · see citing works

Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

(2018) · doi:10.1145/3233547 · Cited by 106 · see citing works

Implications of <i>AlphaFold</i> 2 for crystallographic phasing by molecular replacement

McCoy, A. J., Sammito, M. D., & Read, R. J. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321012122 · Cited by 104 · see citing works

Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning

Subramaniya, S. R. M. V., Terashi, G., & Kihara, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0500-1 · Cited by 99 · see citing works

A Machine Learning Approach for the Identification of Protein Secondary Structure Elements from Electron Cryo‐Microscopy Density Maps

Si, D., Ji, S., Nasr, K. A., & He, J. J. (2012) · Biopolymers · doi:10.1002/bip.22063 · Cited by 93 · see citing works

CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes

Gupta, M., Azumaya, C. M., Moritz, M., Pourmal, S., Diallo, A., Merz, G. E., Jang, G., Bouhaddou, M., Fossati, A., Brilot, A. F., et al. (2021) · doi:10.1101/2021.05.10.443524 · Cited by 89 · see citing works

Classification of crystallization outcomes using deep convolutional neural networks

Bruno, A. E., Charbonneau, P., Newman, J. M., Snell, E. H., So, D. R., Vanhoucke, V., Watkins, C. J. C. H., Williams, S. P., & Wilson, J. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198883 · Cited by 86 · see citing works

High‐accuracy protein structures by combining machine‐learning with physics‐based refinement

Heo, L., & Feig, M. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25847 · Cited by 76 · see citing works

Extraction of protein dynamics information from cryo-EM maps using deep learning

Matsumoto, S., Ishida, S., Araki, M., Kato, T., Terayama, K., & Okuno, Y. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00290-y · Cited by 75 · see citing works

Predictive models for protein crystallization

Rupp, B. (2004) · Methods · doi:10.1016/j.ymeth.2004.03.031 · Cited by 72 · see citing works

PIXER: an automated particle-selection method based on segmentation using a deep neural network

Zhang, J., Wang, Z., Chen, Y., et al. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2614-y · Cited by 57 · see citing works

Structure of the mycobacterial ESX-5 type VII secretion system pore complex

Beckham, K. S. H., Ritter, C., Chojnowski, G., Ziemianowicz, D. S., Mullapudi, E., Rettel, M., Savitski, M. M., Mortensen, S. A., Kosiński, J., & Wilmanns, M. (2021) · Science Advances · doi:10.1126/sciadv.abg9923 · Cited by 57 · see citing works

AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images

Al-Azzawi, A., Ouadou, A., Tanner, J. J., et al. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2926-y · Cited by 53 · see citing works

Crystallographic molecular replacement using an in silico‐generated search model of SARS‐CoV‐2 ORF8

Flower, T. G., & Hurley, J. H. (2021) · Protein Science · doi:10.1002/pro.4050 · Cited by 49 · see citing works

EMNUSS: a deep learning framework for secondary structure annotation in cryo-EM maps

He, J., & Huang, S. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab156 · Cited by 31 · see citing works

Detection oftrans–cisflips and peptide-plane flips in protein structures

Touw, W. G., Joosten, R. P., & Vriend, G. (2015) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s1399004715008263 · Cited by 26 · see citing works

DeepCentering: fully automated crystal centering using deep learning for macromolecular crystallography

Itô, S., Ueno, G., & Yamamoto, M. (2019) · Journal of Synchrotron Radiation · doi:10.1107/s160057751900434x · Cited by 26 · see citing works

Exploiting prior knowledge about biological macromolecules in cryo-EM structure determination

Kimanius, D., Zickert, G., Nakane, T., Adler, J., Lunz, S., Schönlieb, C., Öktem, O., & Scheres, S. H. W. (2020) · IUCrJ · doi:10.1107/s2052252520014384 · Cited by 25 · see citing works

AUSPEX : a graphical tool for X-ray diffraction data analysis

Thorn, A., Parkhurst, J. M., Emsley, P., et al. (2017) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s205979831700969x · Cited by 21 · see citing works

CDAE: A Cascade of Denoising Autoencoders for Noise Reduction in the Clustering of Single-Particle Cryo-EM Images

Lei, H., & Yang, Y. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2020.627746 · Cited by 17 · see citing works

Bayesian machine learning improves single-wavelength anomalous diffraction phasing

García-Bonete, M.-J., & Katona, G. (2019) · Acta Crystallographica Section A Foundations and Advances · doi:10.1107/s2053273319011446 · Cited by 14 · see citing works

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo‐Electron Microscopy Maps

Mostosi, P., Schindelin, H., Kollmannsberger, P., et al. (2020) · Angewandte Chemie · doi:10.1002/ange.202000421 · Cited by 11 · see citing works

Machine-learning techniques for macromolecular crystallization data

Gopalakrishnan, V., Livingston, G. R., Hennessy, D. N., et al. (2004) · Acta Crystallographica Section D Biological Crystallography · doi:10.1107/s090744490401683x · Cited by 10 · see citing works

The predictive power of data-processing statistics

Vollmar, M., Parkhurst, J. M., Jaques, D., et al. (2020) · IUCrJ · doi:10.1107/s2052252520000895 · Cited by 8 · see citing works

Neural networks learn the motions of molecular machines

Grant, T. S. (2021) · Nature Methods · doi:10.1038/s41592-021-01235-y · Cited by 6 · see citing works

Detecting ice artefacts in processed macromolecular diffraction data with machine learning

Nolte, K., Gao, Y., Stäb, S., et al. (2022) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s205979832101202x · Cited by 6 · see citing works

Machine learning to estimate the local quality of protein crystal structures

Miyaguchi, I., Sato, M., Kashima, A., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-02948-y · Cited by 5 · see citing works

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works

PExy: The Other Side of Exploit Kits

Maio, G. D., Kapravelos, A., Shoshitaishvili, Y., et al. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-08509-8_8 · Cited by 24 · see citing works

AEMB: An Automated Exploit Mitigation Bypassing Solution

Wang, R., Pan, Z., Shi, F., et al. (2021) · Applied Sciences · doi:10.3390/app11209727 · Cited by 4 · see citing works

AEG: Automatic Exploit Generation

(2018) · doi:10.1184/r1/6468296.v1 · Cited by 205 · see citing works

Evaluation of Two Host-Based Intrusion Prevention Systems

Labbe, K. G. (2005) · Defense Technical Information Center (DTIC) · Cited by 1 · see citing works

Implementing convolutional neural network model for prediction in medical imaging

Pandey, R., Sahai, A., & Kashyap, H. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824054-0.00024-1 · Cited by 8 · see citing works