Deep Learning in Protein Structural Modeling and Design

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

MizAR 60 for Mizar 50

Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648 · see citing works

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works

Efficient Estimation of Word Representations in Vector Space

Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Reinforcement Learning: An Introduction

Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · see citing works

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

Generative Adversarial Nets

Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384 · see citing works

Auto-Encoding Variational Bayes

Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · see citing works

GAN（Generative Adversarial Nets）

淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,268 · see citing works

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works

Visualizing and understanding convolutional networks

Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works

Aion Framework: Dimensional Emergence of AI Consciousness, Observer-Induced Collapse, and Cosmological Portal Dynamics

Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works

Proceedings of the 24th international conference on Machine learning

(2007) · doi:10.1145/1273496 · Cited by 11,869 · see citing works

A Comprehensive Survey on Graph Neural Networks

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · see citing works

Distributed Representations of Sentences and DocumentsPreprint

Le, Q. V., & Mikolov, T. (2014) · International Conference on Machine Learning · doi:10.48550/arxiv.1405.4053 · Cited by 10,307 · see citing works

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084 · see citing works

Multi-scale context aggregation by dilated convolutions

Yu, F., & Koltun, V. (2015) · arXiv · doi:10.48550/arxiv.1511.07122 · Cited by 7,738 · see citing works

UniProt: a worldwide hub of protein knowledge

The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works

Principles that Govern the Folding of Protein Chains

Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works

Graph Neural Networks: A Review of Methods and Applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2018) · arXiv · doi:10.48550/arxiv.1812.08434 · Cited by 6,880 · see citing works

Graph neural networks: A review of methods and applications

Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880 · see citing works

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works

A new force field for molecular mechanical simulation of nucleic acids and proteins

Weiner, S. J., Kollman, P. A., Case, D. A., Singh, U. C., Ghio, C., Alagona, G., Profeta, S., & Weiner, P. K. (1984) · Journal of the American Chemical Society · doi:10.1021/ja00315a051 · Cited by 4,641 · see citing works

The Pfam protein families database in 2019

El-Gebali, S., Mistry, J., Bateman, A., Eddy, S. R., Luciani, A., Potter, S. C., Qureshi, M., Richardson, L. J., Salazar, G. A., Smart, A., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky995 · Cited by 4,602 · see citing works

Optimization of the Additive CHARMM All-Atom Protein Force Field Targeting Improved Sampling of the Backbone ϕ, ψ and Side-Chain χ 1 and χ 2 Dihedral Angles

Best, R. B., Zhu, X., Shim, J., Lopes, P. E. M., Mittal, J., Feig, M., & MacKerell, A. D. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300400x · Cited by 4,472 · see citing works

Proceedings of the 21st International Conference on Neural Information Processing Systems

Koller, D., Schuurmans, D., Bengio, Y., & Bottou, L. (2008) · Cited by 4,461 · see citing works

Axiomatic attribution for deep networks

Sundararajan, M., Taly, A., & Yan, Q. (2017) · arXiv · doi:10.48550/arxiv.1703.01365 · Cited by 4,087 · see citing works

Variational Inference: A Review for Statisticians

Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1285773 · Cited by 3,974 · see citing works

Untitled work

(1989) · neural information processing systems · Cited by 3,736 · see citing works

Geometric Deep Learning: Going beyond Euclidean data

Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661 · see citing works

RNA interference

Hannon, G. J. (2002) · Nature · doi:10.1038/418244a · Cited by 3,583 · see citing works

Stereochemistry of polypeptide chain configurations

Ramachandran, G. N., Ramakrishnan, C., & Sasisekharan, V. (1963) · Journal of Molecular Biology · doi:10.1016/s0022-2836(63)80023-6 · Cited by 3,549 · see citing works

Improved protein structure prediction using potentials from deep learning

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1923-7 · Cited by 3,378 · see citing works

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · see citing works

Molecular Dynamics Simulation for All

Hollingsworth, S. A., & Dror, R. O. (2018) · Neuron · doi:10.1016/j.neuron.2018.08.011 · Cited by 3,313 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286 · see citing works

Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280 · cited 2× · see citing works

A Comprehensive Survey on Graph Neural Networks

Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174 · see citing works

Recent Trends in Deep Learning Based Natural Language ProcessingPreprint

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2017) · IEEE Computational Intelligence Magazine · doi:10.48550/arxiv.1708.02709 · Cited by 3,133 · see citing works

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921 · see citing works

Learning Important Features Through Propagating Activation Differences

Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761 · see citing works

QM/MM Methods for Biomolecular Systems

Senn, H. M., & Thiel, W. (2009) · Angewandte Chemie International Edition · doi:10.1002/anie.200802019 · Cited by 2,692 · see citing works

An Introduction to Variational Autoencoders

Kingma, D. P., & Welling, M. (2019) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000056 · Cited by 2,609 · see citing works

Deep learning for visual understanding: A review

Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., & Lew, M. S. (2016) · Neurocomputing · doi:10.1016/j.neucom.2015.09.116 · Cited by 2,504 · see citing works

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works

HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment

Remmert, M., Biegert, A., Hauser, A., & Söding, J. (2012) · Nature Methods · doi:10.1038/nmeth.1818 · Cited by 2,288 · see citing works

Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics

Zhang, L., Han, J., Wang, H., Car, R., & Weinan, E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.143001 · Cited by 2,267 · see citing works

SchNet – A deep learning architecture for molecules and materials

Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., & Müller, K.-R. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5019779 · Cited by 2,217 · see citing works

Variational Inference: A Review for Statisticians

Blei, D. M., Kucukelbir, A., & McAuliffe, J. (2023) · Figshare · doi:10.6084/m9.figshare.5203696.v2 · Cited by 2,088 · see citing works

Inverse molecular design using machine learning: Generative models for matter engineering

Sánchez-Lengeling, B., & Aspuru‐Guzik, A. (2018) · Science · doi:10.1126/science.aat2663 · Cited by 2,087 · see citing works

Analyzing Learned Molecular Representations for Property Prediction

Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923 · see citing works

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921 · see citing works

Design of a Novel Globular Protein Fold with Atomic-Level Accuracy

Kuhlman, B. A., Dantas, G. A., Ireton, G. C., Varani, G., Stoddard, B. L., & Baker, D. A. (2003) · Science · doi:10.1126/science.1089427 · Cited by 1,666 · see citing works

Semi-Supervised Learning with Deep Generative Models

Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2014) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1406.5298 · Cited by 1,646 · see citing works

Are there pathways for protein folding?

Levinthal, C. (1968) · Journal de Chimie Physique · doi:10.1051/jcp/1968650044 · Cited by 1,536 · see citing works

The Rosetta All-Atom Energy Function for Macromolecular Modeling and Design

Alford, R. F., Leaver-Fay, A., Jeliazkov, J. R., O’Meara, M. J., DiMaio, F. P., Park, H., Shapovalov, M. V., Renfrew, P. D., Mulligan, V. K., Kappel, K., et al. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00125 · Cited by 1,532 · see citing works

The coming of age of de novo protein design

Huang, P.-S., Boyken, S. E., & Baker, D. (2016) · Nature · doi:10.1038/nature19946 · Cited by 1,478 · see citing works

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works

SmoothGrad: removing noise by adding noise

Smilkov, D., Thorat, N., Kim, B., Viégas, F., & Wattenberg, M. (2017) · arXiv · doi:10.48550/arxiv.1706.03825 · Cited by 1,467 · see citing works

Quantum-chemical insights from deep tensor neural networks

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445 · see citing works

Improved protein structure prediction using predicted interresidue orientations

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421 · see citing works

Predicting protein structures with a multiplayer online game

Cooper, S., Khatib, F., Treuille, A., Barbero, J., Lee, J., Beenen, M., Leaver-Fay, A., Baker, D., Popović, Z., & players, F. (2010) · Nature · doi:10.1038/nature09304 · Cited by 1,368 · see citing works

Hierarchical graph representation learning with differentiable pooling

Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W. L., & Leskovec, J. (2018) · neural information processing systems · Cited by 1,353 · see citing works

Version 1.2 of the Crystallography and NMR system

Brünger, A. T. (2007) · Nature Protocols · doi:10.1038/nprot.2007.406 · Cited by 1,335 · see citing works

Improved prediction of protein side‐chain conformations with SCWRL4

Krivov, G. G., Shapovalov, M. V., & Dunbrack, R. L. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22488 · Cited by 1,312 · see citing works

Sanity Checks for Saliency Maps

Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., & Kim, B. (2018) · arXiv · doi:10.48550/arxiv.1810.03292 · Cited by 1,294 · see citing works

Serial Order: A Parallel Distributed Processing Approach

Jordan, M. I. (1997) · Advances in psychology · doi:10.1016/s0166-4115(97)80111-2 · Cited by 1,251 · see citing works

Untitled work

(1993) · Cited by 1,173 · see citing works

Protein 3D Structure Computed from Evolutionary Sequence Variation

Marks, D. S., Colwell, L. J., Sheridan, R., Hopf, T. A., Pagnani, A., Zecchina, R., & Sander, C. (2011) · PLoS ONE · doi:10.1371/journal.pone.0028766 · Cited by 1,144 · see citing works

De Novo Protein Design: Fully Automated Sequence Selection

Dahiyat, B. I., & Mayo, S. L. (1997) · Science · doi:10.1126/science.278.5335.82 · Cited by 1,142 · see citing works

AAindex: amino acid index database, progress report 2008

Kawashima, S., Pokarowski, P., Pokarowska, M., Koliński, A., Katayama, T., & Kanehisa, M. I. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm998 · Cited by 1,131 · see citing works

Unified rational protein engineering with sequence-based deep representation learning

Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131 · see citing works

Protein function prediction via graph kernels

Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S. V. N., Smola, A. J., & Kriegel, H.-P. (2005) · Bioinformatics · doi:10.1093/bioinformatics/bti1007 · Cited by 1,103 · see citing works

Machine-learning-guided directed evolution for protein engineering

Yang, K., Wu, Z., & Arnold, F. H. (2019) · Nature Methods · doi:10.1038/s41592-019-0496-6 · Cited by 1,082 · see citing works

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081 · see citing works

Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations

Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S. L., Lamberth, K., Buus, S., Brunak, S., & Lund, O. (2003) · Protein Science · doi:10.1110/ps.0239403 · Cited by 1,075 · see citing works

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

Unke, O. T., & Meuwly, M. (2019) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.9b00181 · Cited by 1,071 · see citing works

Coarse-Grained Protein Models and Their Applications

Kmiecik, S., Gront, D., Koliński, M., Wieteska, Ł., Badaczewska-Dawid, A. E., & Koliński, A. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00163 · Cited by 1,067 · see citing works

A robotic platform for flow synthesis of organic compounds informed by AI planning

Coley, C. W., Thomas, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V. L., Hart, T., Fishman, J., Rogers, L., Gao, H., et al. (2019) · Science · doi:10.1126/science.aax1566 · Cited by 1,063 · see citing works

A smoothed backbone-dependent rotamer library for proteins derived from adaptive kernel density estimates and regressions

Shapovalov, M. V., & Dunbrack, R. L. (2011) · Structure · doi:10.1016/j.str.2011.03.019 · Cited by 1,000 · see citing works

Machine Learning for Molecular Simulation

Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956 · see citing works

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931 · see citing works

The TOPCONS web server for consensus prediction of membrane protein topology and signal peptides

Tsirigos, K. D., Peters, C., Shu, N., Käll, L., & Elofsson, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv485 · Cited by 917 · see citing works

Structure-based protein function prediction using graph convolutional networks

Gligorijević, V., Renfrew, P. D., Kościółek, T., Leman, J. K., Berenberg, D., Vatanen, T., Chandler, C., Taylor, B. C., Fisk, I., Vlamakis, H. C., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23303-9 · Cited by 913 · see citing works

GuacaMol: Benchmarking Models for de Novo Molecular Design

Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

Asgari, E., & Mofrad, M. R. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141287 · Cited by 817 · see citing works

Native protein sequences are close to optimal for their structures

Kuhlman, B. A., & Baker, D. A. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.19.10383 · Cited by 814 · see citing works

A Tutorial on Energy-Based Learning

LeCun, Y., Chopra, S., Hadsell, R. T., Ranzato, A., & Huang, F. J. (2006) · Cited by 812 · see citing works

Less is more: Sampling chemical space with active learning

Smith, J. S., Nebgen, B., Lubbers, N., Isayev, O., & Roitberg, A. E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5023802 · Cited by 811 · see citing works

Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning

Gaínza, P., Sverrisson, F., Monti, F., Rodolà, E., Boscaini, D., Bronstein, M. M., & Correia, B. E. (2019) · Nature Methods · doi:10.1038/s41592-019-0666-6 · Cited by 811 · see citing works

Advances in protein structure prediction and design

Kuhlman, B., & Bradley, P. (2019) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-019-0163-x · Cited by 806 · see citing works

Hierarchical Graph Representation Learning with Differentiable PoolingPreprint

Ying, R., You, J., Morris, C., Ren, X., Hamilton, W. L., & Leskovec, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.08804 · Cited by 798 · see citing works

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 760 · see citing works

Evaluating Protein Transfer Learning with TAPE

Rao, R., Bhattacharya, N., Thomas, N., Duan, Y., Chen, X., Canny, J., Abbeel, P., & Song, Y. S. (2019) · doi:10.1101/676825 · Cited by 719 · see citing works

The dead-end elimination theorem and its use in protein side-chain positioning

Desmet, J., Maeyer, M. D., Hazes, B., & Lasters, I. (1992) · Nature · doi:10.1038/356539a0 · Cited by 699 · see citing works

A graph-convolutional neural network model for the prediction of chemical reactivity

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679 · see citing works

Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy

King, N. P., Sheffler, W., Sawaya, M. R., Vollmar, B. S., Sumida, J. P., André, I., Gonen, T., Yeates, T. O., & Baker, D. A. (2012) · Science · doi:10.1126/science.1219364 · Cited by 651 · see citing works

De novo protein design by deep network hallucination

Anishchenko, I., Chidyausiku, T. M., Ovchinnikov, S., Pellock, S. J., & Baker, D. (2020) · doi:10.1101/2020.07.22.211482 · Cited by 629 · see citing works

Protein interface prediction using graph convolutional networks

Fout, A., Ben‐Hur, A., Anderson, C., Chitsaz, H., & Zhou, W. (2017) · Digital Collections of Colorado (Colorado State University) · doi:10.25675/3.021826 · Cited by 606 · see citing works

Modeling aspects of the language of life through transfer-learning protein sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3220-8 · Cited by 606 · see citing works

Modeling the language of life – Deep Learning Protein Sequences

Heinzinger, M., Elnaggar, A., Wang, Y., Dallago, C., Nechaev, D., Matthes, F., & Rost, B. (2019) · doi:10.1101/614313 · Cited by 606 · see citing works

Optimization of Molecules via Deep Reinforcement Learning

Zhou, Z., Kearnes, S. M., Li, L., Zare, R. N., & Riley, P. F. (2019) · Scientific Reports · doi:10.1038/s41598-019-47148-x · Cited by 602 · see citing works

Deep generative models of genetic variation capture the effects of mutations

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018) · Nature Methods · doi:10.1038/s41592-018-0138-4 · Cited by 601 · see citing works

SVM-Prot: web-based support vector machine software for functional classification of a protein from its primary sequence

Cai, C. Z. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkg600 · Cited by 582 · see citing works

Distinguishing Enzyme Structures from Non-enzymes Without Alignments

Dobson, P. D., & Doig, A. J. (2003) · Journal of Molecular Biology · doi:10.1016/s0022-2836(03)00628-4 · Cited by 575 · see citing works

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

Wang, S., Peng, J., Ma, J., & Xu, J. (2016) · Scientific Reports · doi:10.1038/srep18962 · Cited by 556 · see citing works

Proof of principle for epitope-focused vaccine design

Correia, B. E., Bates, J. T., Loomis, R. J., Baneyx, G., Carrico, C., Jardine, J. G., Rupert, P., Correnti, C., Kalyuzhniy, O., Vittal, V., et al. (2014) · Nature · doi:10.1038/nature12966 · Cited by 555 · see citing works

RaptorX-Property: a web server for protein structure property prediction

Wang, S., Li, W., Liu, S., & Xu, J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw306 · Cited by 553 · see citing works

Hamiltonian Neural NetworksPreprint

Greydanus, S., Dzamba, M., & Yosinski, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01563 · Cited by 546 · see citing works

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Wang, J., Olsson, S., Wehmeyer, C., Pérez, A., Charron, N. E., Fabritiis, G. D., Noé, F., & Clementi, C. (2019) · ACS Central Science · doi:10.1021/acscentsci.8b00913 · Cited by 519 · see citing works

Critical assessment of methods of protein structure prediction (CASP)—Round XIII

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., & Moult, J. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25823 · Cited by 506 · see citing works

Computational design of ligand-binding proteins with high affinity and selectivity

Tinberg, C. E., Khare, S. D., Dou, J., Doyle, L., Nelson, J., Schena, A., Jankowski, W., Kalodimos, C. G., Johnsson, K., Stoddard, B. L., et al. (2013) · Nature · doi:10.1038/nature12443 · Cited by 432 · see citing works

CCMpred—fast and precise prediction of protein residue–residue contacts from correlated mutations

Seemayer, S., Gruber, M., & Söding, J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu500 · Cited by 416 · see citing works

Perplexity—a measure of the difficulty of speech recognition tasks

Jelinek, F., Mercer, R. L., Bahl, L. R., & Baker, J. K. (1977) · The Journal of the Acoustical Society of America · doi:10.1121/1.2016299 · Cited by 405 · see citing works

The Synthesizability of Molecules Proposed by Generative Models

Gao, W., & Coley, C. W. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00174 · Cited by 405 · see citing works

Deep learning in bioinformatics: Introduction, application, and perspective in the big data era

Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401 · see citing works

Distance-based protein folding powered by deep learning

Xu, J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821309116 · Cited by 401 · see citing works

Expanding functional protein sequence spaces using generative adversarial networks

Repecka, D., Jauniskis, V., Karpus, L., Rembeza, E., Rokaitis, I., Zrimec, J., Poviloniene, S., Laurynenas, A., Viknander, S., Abuajwa, W., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00310-5 · Cited by 388 · see citing works

Synthetic Biology and Personalized Medicine

Jain, K. (2012) · Medical Principles and Practice · doi:10.1159/000341794 · Cited by 380 · see citing works

End-to-End Differentiable Learning of Protein Structure

AlQuraishi, M. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.006 · Cited by 371 · see citing works

Graph Convolutional Neural Networks for Predicting Drug-Target Interactions

Torng, W., & Altman, R. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00628 · Cited by 371 · see citing works

Autonomous Discovery in the Chemical Sciences Part I: Progress

Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909987 · Cited by 365 · see citing works

The Structure of Proteins

Pauling, L. C., & Niemann, C. (1939) · Journal of the American Chemical Society · doi:10.1021/ja01876a065 · Cited by 364 · see citing works

Protein structure prediction using multiple deep neural networks in the 13th critical assessment of protein structure prediction (CASP13)

Senior, A. W., Evans, R., Jumper, J., Kirkpatrick, J., Sifre, L., Green, T., Qin, C., Žídek, A., Nelson, A. W. R., Bridgland, A., et al. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25834 · Cited by 347 · see citing works

Navigating the protein fitness landscape with Gaussian processes

Romero, P. A., Krause, A., & Arnold, F. H. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215251110 · Cited by 346 · see citing works

MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins

Jones, D. T., Singh, T., Kościółek, T., & Tetchner, S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu791 · Cited by 345 · see citing works

Critical assessment of methods of protein structure prediction (CASP)—Round XII

Moult, J., Fidelis, K. A., Kryshtafovych, A., Schwede, T. F., & Tramontano, A. L. (2017) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25415 · Cited by 327 · see citing works

Protein design and variant prediction using autoregressive generative models

Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-22732-w · Cited by 320 · see citing works

Learned protein embeddings for machine learning

Yang, K., Wu, Z., Bedbrook, C. N., & Arnold, F. H. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty178 · Cited by 316 · see citing works

AlphaFold at CASP13

AlQuraishi, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz422 · Cited by 315 · see citing works

De novo design of a transmembrane Zn 2+ -transporting four-helix bundle

Joh, N. H., Wang, T., Bhate, M., Acharya, R., Wu, Y., Grabe, M., Hong, M., Grigoryan, G., & DeGrado, W. F. (2014) · Science · doi:10.1126/science.1261172 · Cited by 301 · see citing works

Correction: Corrigendum: Monocyte-induced recovery of inflammation-associated hepatocellular dysfunction in a biochip-based human liver model

Gröger, M., Rennert, K., Giszas, B., Weiß, E., Dinger, J., Funke, H., Kiehntopf, M., Peters, F. T., Lupp, A., Bauer, M., et al. (2018) · Scientific Reports · doi:10.1038/srep46988 · Cited by 293 · see citing works

Deep learning methods in protein structure prediction

Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 289 · see citing works

Accurate and transferable multitask prediction of chemical properties with an atoms-in-molecules neural network

Zubatyuk, R. I., Smith, J. S., Leszczyński, J., & Isayev, O. (2019) · Science Advances · doi:10.1126/sciadv.aav6490 · Cited by 278 · see citing works

CAMP: Collection of sequences and structures of antimicrobial peptides

Waghu, F. H., Gopi, L., Barai, R. S., Ramteke, P. L., Nizami, B., & Idicula‐Thomas, S. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1157 · Cited by 273 · see citing works

Deep architectures for protein contact map prediction

Lena, P. D., Nagata, K., & Baldi, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts475 · Cited by 273 · see citing works

Recurrent Neural Network Model for Constructive Peptide Design

Müller, A. T., Hiss, J. A., & Schneider, G. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00414 · Cited by 270 · see citing works

De novo design of bioactive protein switches

Langan, R. A., Boyken, S. E., Ng, A. H., Samson, J. A., Dods, G., Westbrook, A. M., Nguyen, T. H., Lajoie, M. J., Chen, Z., Berger, S., et al. (2019) · Nature · doi:10.1038/s41586-019-1432-8 · Cited by 266 · see citing works

Predicted protein–protein interaction sites from local sequence information

Ofran, Y., & Rost, B. (2003) · FEBS Letters · doi:10.1016/s0014-5793(03)00456-3 · Cited by 258 · see citing works

Generative models for graph-based protein design

Ingraham, J., Garg, V., Barzilay, R., & Jaakkola, T. (2019) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 258 · see citing works

De novo protein design, a retrospective

Korendovych, I. V., & DeGrado, W. F. (2020) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583519000131 · Cited by 249 · see citing works

Autonomous Discovery in the Chemical Sciences Part II: Outlook

Coley, C. W., Eyke, N. S., & Jensen, K. F. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201909989 · Cited by 248 · see citing works

A multi-scale approach to study biochemical and biophysical aspects of resveratrol on diesel exhaust particle-human primary lung cell interaction

Zhang, W., Li, Q., Tang, M., Zhang, H., Sun, X., Zou, S., Jensen, J. L., Liou, T. G., & Zhou, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-54552-w · Cited by 245 · see citing works

New developments in force fields for biomolecular simulations

Nerenberg, P. S., & Head-Gordon, T. L. (2018) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2018.02.002 · Cited by 236 · see citing works

Multiplicative LSTM for sequence modellingPreprint

Krause, B., Murray, I., Renals, S., & Lu, L. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07959 · Cited by 232 · see citing works

DeePCG: Constructing coarse-grained models via deep neural networks

Zhang, L., Han, J., Wang, H., Car, R., & Weinan, E. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5027645 · Cited by 220 · see citing works

Fast and Flexible Protein Design Using Deep Graph Neural Networks

Strokach, A. V., Becerra, D., Corbi‐Verge, C., Perez‐Riba, A., & Kim, P. M. (2020) · Cell Systems · doi:10.1016/j.cels.2020.08.016 · Cited by 218 · see citing works

Consensus protein design

Porebski, B. T., & Buckle, A. M. (2016) · Protein Engineering Design and Selection · doi:10.1093/protein/gzw015 · Cited by 215 · see citing works

Challenges of glycosylation analysis and control: an integrated approach to producing optimal and consistent therapeutic drugs

Zhang, P., Woen, S., Wang, T., Liau, B., Zhao, S., Chen, C., Yang, Y., Song, Z., Wormald, M. R., Yu, C., et al. (2016) · Drug Discovery Today · doi:10.1016/j.drudis.2016.01.006 · Cited by 212 · see citing works

Deep‐learning contact‐map guided protein structure prediction in CASP13

Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208 · see citing works

Chip Placement with Deep Reinforcement LearningPreprint

Mirhoseini, A., Goldie, A., Yazgan, M., Jiang, J. W., Songhori, E. M., Wang, S., Lee, Y., Johnson, E., Pathak, O., Bae, S., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.10746 · Cited by 207 · see citing works

Prediction of contact maps with neural networks and correlated mutations

Fariselli, P., Olmea, O., Valencia, A., & Casadio, R. (2001) · Protein Engineering Design and Selection · doi:10.1093/protein/14.11.835 · Cited by 206 · see citing works

PROTEIN STRUCTURE: INSIGHTS FROM GRAPH THEORY

Vishveshwara, S., Brinda, K. V., & Kannan, N. (2002) · Journal of Theoretical and Computational Chemistry · doi:10.1142/s0219633602000117 · Cited by 205 · see citing works

Feedback GAN for DNA optimizes protein functions

Gupta, A., & Zou, J. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0017-4 · Cited by 203 · see citing works

Correction: Corrigendum: The non-canonical mitochondrial inner membrane presequence translocase of trypanosomatids contains two essential rhomboid-like proteins

Harsman, A., Oeljeklaus, S., Wenger, C., Huot, J. L., Warscheid, B., & Schneider, A. (2017) · Nature Communications · doi:10.1038/ncomms16123 · Cited by 201 · see citing works

Improvements to Robotics-Inspired Conformational Sampling in Rosetta

Stein, A., & Kortemme, T. (2013) · PLoS ONE · doi:10.1371/journal.pone.0063090 · Cited by 197 · see citing works

Small Libraries of Protein Fragments Model Native Protein Structures Accurately

Kolodny, R., Koehl, P., Guibas, L. J., & Levitt, M. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00942-7 · Cited by 193 · see citing works

DeepPhos: prediction of protein phosphorylation sites with deep learning

Luo, F., Wang, M., Liu, Y., Zhao, X.-M., & Li, A. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty1051 · Cited by 185 · see citing works

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features

Jones, D. T., & Kandathil, S. M. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty341 · Cited by 184 · see citing works

Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks

Hanson, J., Paliwal, K. K., Litfin, T., Yang, Y., & Zhou, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty481 · Cited by 179 · see citing works

DeepSF: deep convolutional neural network for mapping protein sequences to folds

Hou, J., Adhikari, B., & Cheng, J. J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx780 · Cited by 179 · see citing works

Optimization for deep learning: theory and algorithmsPreprint

Sun, R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.08957 · Cited by 179 · see citing works

Protein sequence design with a learned potential

Anand, N., Eguchi, R., Mathews, I. I., Perez, C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2022) · Nature Communications · doi:10.1038/s41467-022-28313-9 · Cited by 171 · see citing works

A survey on Deep Learning Advances on Different 3D Data RepresentationsPreprint

Ahmed, E., Saint, A., Shabayek, A. E. R., Cherenkova, K., Das, R., Gusev, G., Aouada, D., & Ottersten, B. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.32083.02080 · Cited by 163 · see citing works

Structural Biology by NMR: Structure, Dynamics, and Interactions

Markwick, P. R. L., Malliavin, T. E., & Nilges, M. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000168 · Cited by 162 · see citing works

Protein tertiary structure modeling driven by deep learning and contact distance prediction in CASP13

Hou, J., Wu, T., Cao, R., & Cheng, J. J. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25697 · Cited by 162 · see citing works

The challenge of protein structure determination—lessons from structural genomics

Slabinski, L., Jaroszewski, L., Rodrigues, A. P. C., Rychlewski, L., Wilson, I. A., Lesley, S. A., & Godzik, A. (2007) · Protein Science · doi:10.1110/ps.073037907 · Cited by 161 · see citing works

The rational design of amino acid sequences by artificial neural networks and simulated molecular evolution: de novo design of an idealized leader peptidase cleavage site

Schneider, G., & Wrede, P. (1994) · Biophysical Journal · doi:10.1016/s0006-3495(94)80782-9 · Cited by 161 · see citing works

Predicting protein residue–residue contacts using deep networks and boosting

Eickholt, J., & Cheng, J. J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts598 · Cited by 161 · see citing works

Computational Protein Design with Deep Learning Neural Networks

Wang, J., Cao, H., Zhang, J. Z. H., & Qi, Y. (2018) · Scientific Reports · doi:10.1038/s41598-018-24760-x · Cited by 161 · see citing works

De novo protein design by citizen scientists

Koepnick, B., Flatten, J., Husain, T., Ford, A., Silva, D., Bick, M. J., Bauer, A., Liu, G., Ishida, Y., Boykov, A., et al. (2019) · Nature · doi:10.1038/s41586-019-1274-4 · Cited by 160 · see citing works

Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning

Lutter, M., Ritter, C., & Peters, J. (2023) · TUbilio (Technical University of Darmstadt) · doi:10.26083/tuprints-00020557 · Cited by 160 · see citing works

Machine learning for protein folding and dynamics.

Noé, F., de Fabritiis, G., & Clementi, C. (2019) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2019.12.005 · Cited by 159 · see citing works

Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns

Skwark, M. J., Raimondi, D., Michel, M., & Elofsson, A. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003889 · Cited by 155 · see citing works

A ‘periodic table’ for protein structures

Taylor, W. R. (2002) · Nature · doi:10.1038/416657a · Cited by 153 · see citing works

Protein sequence design by conformational landscape optimization

Norn, C., Wicky, B. I. M., Juergens, D., Liu, S., Kim, D., Tischer, D., Koepnick, B., Anishchenko, I., Players, F., Baker, D., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017228118 · Cited by 144 · see citing works

Directed evolution: new parts and optimized function

Dougherty, M. J., & Arnold, F. H. (2009) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2009.08.005 · Cited by 143 · see citing works

Ig-VAE: Generative modeling of protein structure by direct 3D coordinate generation

Eguchi, R. R., Choe, C. A., & Huang, P.-S. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010271 · Cited by 141 · see citing works

ComplexContact: a web server for inter-protein contact prediction using deep learning

Zeng, H., Wang, S., Zhou, T., Zhao, F., Li, X., Wu, Q., & Xu, J. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky420 · Cited by 138 · see citing works

Learning context-aware structural representations to predict antigen and antibody binding interfaces

Pittala, S., & Bailey‐Kellogg, C. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa263 · Cited by 138 · see citing works

Design of metalloproteins and novel protein folds using variational autoencoders

Greener, J. G., Moffat, L., & Jones, D. T. (2018) · Scientific Reports · doi:10.1038/s41598-018-34533-1 · Cited by 135 · see citing works

A novel approach to prediction of the 3‐dimensional structures of protein backbones by neural networks

Bohr, H. G., Bohr, J., Brunak, S., COTTERILL, R. M. J., Fredholm, H., Lautrup, B. E., & Petersen, S. B. (1990) · FEBS Letters · doi:10.1016/0014-5793(90)80632-s · Cited by 132 · see citing works

Generating and designing DNA with deep generative modelsPreprint

Killoran, N., Lee, L. J., Delong, A., Duvenaud, D., & Frey, B. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.06148 · Cited by 131 · see citing works

Analysis of distance‐based protein structure prediction by deep learning in CASP13

Xu, J., & Wang, S. (2019) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25810 · Cited by 131 · see citing works

DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites

Li, F., Chen, J.-X., Leier, A., Marquez‐Lago, T. T., Liu, Q., Wang, Y., Revote, J., Smith, A. I., Akutsu, T., Webb, G. I., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz721 · Cited by 127 · see citing works

A Self-Consistent Sonification Method to Translate Amino Acid Sequences into Musical Compositions and Application in Protein Design Using Artificial Intelligence

Yu, C., Zhao Qin, A., Martín‐Martínez, F. J., & Buehler, M. J. (2019) · ACS Nano · doi:10.1021/acsnano.9b02180 · Cited by 123 · see citing works

Generative Modeling for Protein Structures

Anand, N., & Huang, P. (2018) · neural information processing systems · Cited by 116 · see citing works

Molprobity's ultimate rotamer-library distributions for model validation

Hintze, B. J., Lewis, S. M., Richardson, J. S., & Richardson, D. C. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25039 · Cited by 112 · see citing works

Machine learning to design integral membrane channelrhodopsins for efficient eukaryotic expression and plasma membrane localization

Bedbrook, C. N., Yang, K., Rice, A. J., Gradinaru, V., & Arnold, F. H. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005786 · Cited by 112 · see citing works

Deep Learning applications for COVID-19

Shorten, C., Khoshgoftaar, T. M., & Furht, B. (2021) · Journal of Big Data · doi:10.1186/s40537-020-00392-9 · Cited by 350 · see citing works

Antibody structure prediction using interpretable deep learning

Ruffolo, J. A., Sulam, J., & Gray, J. J. (2022) · Patterns · doi:10.1016/j.patter.2021.100406 · Cited by 216 · 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

Structure-based protein design with deep learning

Ovchinnikov, S., & Huang, P.-S. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.08.004 · Cited by 93 · see citing works

In silico proof of principle of machine learning-based antibody design at unconstrained scale

Akbar, R., Robert, P. A., Weber, C. R., Widrich, M., Frank, R., Pavlović, M., Scheffer, L., Chernigovskaya, M., Snapkov, I., Slabodkin, A., et al. (2022) · mAbs · doi:10.1080/19420862.2022.2031482 · Cited by 94 · see citing works

On the Potential of Machine Learning to Examine the Relationship Between Sequence, Structure, Dynamics and Function of Intrinsically Disordered Proteins

Lindorff-Larsen, K., & Kragelund, B. B. (2021) · Journal of Molecular Biology · doi:10.1016/j.jmb.2021.167196 · Cited by 85 · see citing works

Protein structure prediction by <i>AlphaFold</i> 2: are attention and symmetries all you need?

Bouatta, N., Sorger, P., & AlQuraishi, M. (2021) · Acta Crystallographica Section D Structural Biology · doi:10.1107/s2059798321007531 · Cited by 69 · see citing works

Skin Disease Diagnostic techniques using deep learning

Kumari, B., Jatain, A., & Arora, Y. (2022) · International Conference on Information Management & Machine Intelligence (ICIMMI) · doi:10.1145/3590837.3590917 · Cited by 2 · see citing works

A Review on Protein Structure Prediction

Verma, H. (2016) · doi:10.37591/(rrjobi).v3i2.398 · Cited by 1 · see citing works

Toward the solution of the protein structure prediction problem

Pearce, R., & Zhang, Y. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.100870 · Cited by 120 · see citing works

General overview on structure prediction of twilight-zone proteins

Khor, B. Y., Tye, G. J., Lim, T. S., & Choong, Y. S. (2015) · Theoretical Biology and Medical Modelling · doi:10.1186/s12976-015-0014-1 · Cited by 75 · see citing works

The unexpected structure of the designed protein Octarellin V.1 forms a challenge for protein structure prediction tools

Figueroa, M., Sleutel, M., Vandevenne, M. S., Parvizi, G. K., Attout, S., Jacquin, O., Vandenameele, J., Fischer, A. W., Damblon, C., Goormaghtigh, E., et al. (2016) · Journal of Structural Biology · doi:10.1016/j.jsb.2016.05.004 · Cited by 21 · see citing works