End-to-End Differentiable Learning of Protein Structure

AlQuraishi, Mohammed·2019·Cell Systems

Cited by (374)

Items 101–200 of 374

  1. Recent developments in deep learning applied to protein structure prediction
    Shaun M. Kandathil, Joe G. Greener, David T. Jones · 2019 · Proteins Structure Function and Bioinformatics
  2. An Overview of Alphafold's Breakthrough
    Stefan‐Bogdan Marcu, Sabin Tabirca, Mark Tangney · 2022 · Frontiers in Artificial Intelligence
  3. Integrative Structural Biology in the Era of Accurate Structure Prediction
    Masrati, Gal, Landau, Meytal, Ben-Tal, Nir, Lupas, Andrei, Kosloff, Mickey, Kosinski, Jan · 2021 · Journal of Molecular Biology
  4. Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms
    Bin Huang, Lupeng Kong, Chao Wang, Fusong Ju, Qi Zhang, Jianwei Zhu, Tiansu Gong, Haicang Zhang, Chungong Yu, Wei‐Mou Zheng et al. · 2023 · Genomics Proteomics & Bioinformatics
  5. Attention mechanism enhanced LSTM with residual architecture and its application for protein-protein interaction residue pairs prediction
    Jiale Liu, Xinqi Gong · 2019 · BMC Bioinformatics
  6. Pre-Training of Deep Bidirectional Protein Sequence Representations With Structural Information
    Seonwoo Min, Seunghyun Park, Siwon Kim, Hyun-Soo Choi, Byunghan Lee, Sungroh Yoon · 2021 · IEEE Access
  7. OPUS-TASS: a protein backbone torsion angles and secondary structure predictor based on ensemble neural networks
    Gang Xu, Qinghua Wang, Jianpeng Ma · 2020 · Bioinformatics
  8. Predicting the Real‐Valued Inter‐Residue Distances for Proteins
    Wenze Ding, Haipeng Gong · 2020 · Advanced Science
  9. Reaching alignment-profile-based accuracy in predicting protein secondary and tertiary structural properties without alignment
    Jaspreet Singh, Kuldip K. Paliwal, Thomas Litfin, Jaswinder Singh, Yaoqi Zhou · 2022 · Scientific Reports
  10. Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics
    Ram Narayan Prajapati, Bharat Bhushan, Kuldeep Singh, Himansu Chopra, Shivendra Kumar, Mehak Agrawal, Devender Pathak, Dilip Kumar Chanchal, Laxmi Kant · 2024 · Current Pharmaceutical Biotechnology
  11. Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives
    Manmayee Mohapatra, Chittaranjan Sahu, Snehamayee Mohapatra · 2024 · Current Drug Targets
  12. DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout
    Badri Adhikari · 2019 · Bioinformatics
  13. Metabolomics and modelling approaches for systems metabolic engineering
    Jasmeet Kaur Khanijou, Hanna Kulyk, Cécilia Bergès, Leng Wei Khoo, Pnelope Ng, Hock Chuan Yeo, Mohamed Helmy, Floriant Bellvert, Wee Chew, Kumar Selvarajoo · 2022 · Metabolic Engineering Communications
  14. Machine learning accelerates quantum mechanics predictions of molecular crystals
    Yanqiang Han, Imran Ali, Zhilong Wang, Junfei Cai, Sicheng Wu, Jiequn Tang, Lin Zhang, Jiahao Ren, Rui Xiao, Qianqian Lu et al. · 2021 · Physics Reports
  15. Bioengineering horizon scan 2020
    Luke Kemp, Laura Adam, Christian R. Boehm, Rainer Breitling, Rocco J. Casagrande, Malcolm Dando, Appolinaire Djikeng, Nicholas Greig Evans, Richard Hammond, Kelly Hills et al. · 2020 · eLife
  16. Recent advances and applications of deep learning, electroencephalography, and modern analysis techniques in screening, evaluation, and mechanistic analysis of taste peptides
    Lijun Su, Huizhuo Ji, Jianlei Kong, Wenjing Yan, Qingchuan Zhang, Jian Li, Min Zuo · 2024 · Trends in Food Science & Technology
  17. Advanced AI and ML frameworks for transforming drug discovery and optimization: With innovative insights in polypharmacology, drug repurposing, combination therapy and nanomedicine
    Subiya Ambreen, Mohammad Umar, Aaisha Noor, Himangini Jain, Ruhi Ali · 2024 · European Journal of Medicinal Chemistry
  18. A glance into the evolution of template-free protein structure prediction methodologies
    Surbhi Dhingra, Ramanathan Sowdhamini, Frédéric Cadet, Bernard Offmann · 2020 · Biochimie
  19. Toxicity Prediction Method Based on Multi-Channel Convolutional Neural Network
    Qing Yuan, Zhiqiang Wei, Xu Guan, Mingjian Jiang, Shuang Wang, Shugang Zhang, Zhen Li · 2019 · Molecules
  20. Protein sequence‐to‐structure learning: Is this the end(‐to‐end revolution)?
    Laine, Elodie, Eismann, Stephan, Elofsson, Arne, Grudinin, Sergei · 2021 · Proteins: Structure, Function, and Bioinformatics
  21. Latent generative landscapes as maps of functional diversity in protein sequence space
    Cheyenne Ziegler, Jonathan Martin, Claude Sinner, Faruck Morcos · 2023 · Nature Communications
  22. A fully open-source framework for deep learning protein real-valued distances
    Badri Adhikari · 2020 · Scientific Reports
  23. Leveraging deep learning to improve vaccine design
    Andrew P. Hederman, Margaret E. Ackerman · 2023 · Trends in Immunology
  24. A Generative Model for Molecular Distance Geometry
    Gregor N. C. Simm, José Miguel Hernández-Lobato · 2019 · arXiv (Cornell University)
  25. Learning to evolve structural ensembles of unfolded and disordered proteins using experimental solution data
    Oufan Zhang, Mojtaba Haghighatlari, Jie Li, Zi Hao Liu, Ashley Namini, João M. C. Teixeira, Julie Deborah Forman-Kay, Teresa L. Head-Gordon · 2023 · The Journal of Chemical Physics
  26. Ultrafast end-to-end protein structure prediction enables high-throughput exploration of uncharacterized proteins
    Shaun M. Kandathil, Joe G. Greener, Andy M. Lau, David T. Jones · 2022 · Proceedings of the National Academy of Sciences
  27. Learning Neural Generative Dynamics for Molecular Conformation Generation
    Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang · 2021 · arXiv (Cornell University)
  28. Artificial intelligence method to design and fold alpha-helical structural proteins from the primary amino acid sequence
    Zhao Qin A, Lingfei Wu, Hui Sun, Siyu Huo, Tengfei Ma, Eugene J. Lim, Pin‐Yu Chen, Benedetto Marelli, Markus J. Buehler · 2019 · Extreme Mechanics Letters
  29. Protein structure prediction in the deep learning era
    Zhenling Peng, Wenkai Wang, Renmin Han, Fa Zhang, Jianyi Yang · 2022 · Current Opinion in Structural Biology
  30. Improved structure-related prediction for insufficient homologous proteins using MSA enhancement and pre-trained language model
    Qiaozhen Meng, Fei Guo, Jijun Tang · 2023 · Briefings in Bioinformatics
  31. PyPEF—An Integrated Framework for Data-Driven Protein Engineering
    Niklas E. Siedhoff, Alexander-Maurice Illig, Ulrich Schwaneberg, Mehdi D. Davari · 2021 · Journal of Chemical Information and Modeling
  32. Experimentally-driven protein structure modeling
    Nikolay V. Dokholyan · 2020 · Journal of Proteomics
  33. Graph Neural Networks: A bibliometrics overview
    Abdalsamad Keramatfar, Mohadeseh Rafiee, Hossein Amirkhani · 2022 · Machine Learning with Applications
  34. Fundamentals to function: Quantitative and scalable approaches for measuring protein stability
    Beatriz Atsavapranee, Catherine Stark, Fanny Sunden, Samuel Thompson, Polly Morrell Fordyce · 2021 · Cell Systems
  35. Recent Advances and Challenges in Protein Structure Prediction
    Chunxiang Peng, Fang Liang, Yuhao Xia, Kailong Zhao, Minghua Hou, Guijun Zhang · 2023 · Journal of Chemical Information and Modeling
  36. A machine learning toolkit for genetic engineering attribution to facilitate biosecurity
    Ethan C. Alley, Miles Turpin, Andrew Bo Liu, Taylor Kulp-McDowall, Jacob L. Swett, Rey Edison, Stephen E. Von Stetina, George McDonald Church, Kevin M. Esvelt · 2020 · Nature Communications
  37. Large-scale design and refinement of stable proteins using sequence-only models
    Jedediah M. Singer, Scott Novotney, Devin Strickland, Hugh K. Haddox, Nicholas Leiby, Gabriel J. Rocklin, Cameron M. Chow, Anindya Roy, Asim K. Bera, Francis C. Motta et al. · 2022 · PLoS ONE
  38. AI protein-folding algorithms solve structures faster than ever
    Matthew Hutson · 2019 · Nature
  39. Structure-Based Protein Function Prediction using Graph Convolutional Networks
    Vladimir Gligorijević, P. Douglas Renfrew, Tomasz Kościółek, Julia Koehler Leman, Daniel Berenberg, Tommi Vatanen, Chris Chandler, Bryn C. Taylor, Ian M. Fisk, Hera C. Vlamakis et al. · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
  40. Generative AAV capsid diversification by latent interpolation
    Sam Sinai, Nina K. Jain, George McDonald Church, Eric D. Kelsic · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
  41. Molecular simulation -based research on antifreeze peptides: advances and perspectives
    Wenting Jiang, Fujia Yang, Xu Chen, Xixi Cai, Jinhong Wu, Ming Du, Jianlian Huang, Shaoyun Wang · 2022 · Journal of Future Foods
  42. Multiscale Modeling at the Interface of Molecular Mechanics and Natural Language through Attention Neural Networks
    Markus J. Buehler · 2022 · Accounts of Chemical Research
  43. ProSPr: Democratized Implementation of Alphafold Protein Distance Prediction Network
    W.M. Billings, Bryce Hedelius, Todd Millecam, David Wingate, Dennis Della Corte · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
  44. Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics
    Zachary Smith, Pavan Ravindra, Yihang Wang, Rory Cooley, Pratyush Tiwary · 2020 · The Journal of Physical Chemistry B
  45. Toward machine-guided design of proteins
    Surojit Biswas, Gleb Kuznetsov, Pierce J. Ogden, Nicholas J. Conway, Ryan Prescott Adams, George McDonald Church · 2018 · bioRxiv (Cold Spring Harbor Laboratory)
  46. Geometric Graph Representation Learning on Protein Structure Prediction
    Tian Xia, Wei‐Shinn Ku · 2021 · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)
  47. Machine Learning in Mass Spectrometric Analysis of DIA Data
    Leon L. Xu, Adamo Young, Audrina Zhou, Hannes Luc Röst · 2020 · PROTEOMICS
  48. An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
    Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gómez‐Bombarelli, Jian Tang · 2021 · arXiv (Cornell University)
  49. Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
    Greener, Joe G., Jones, David T. · 2021 · PLOS ONE
  50. High throughput and quantitative enzymology in the genomic era
    D. Mokhtari, M. Appel, P. M. Fordyce, D. Herschlag · 2021 · Current Opinion in Structural Biology
  51. End-to-End Protein Normal Mode Frequency Predictions Using Language and Graph Models and Application to Sonification
    Yiwen Hu, Markus J. Buehler · 2022 · ACS Nano
  52. Prediction Machines: Applied Machine Learning for Therapeutic Protein Design and Development
    Tim J. Kamerzell, Charles Russell Middaugh · 2020 · Journal of Pharmaceutical Sciences
  53. Energy-based models for atomic-resolution protein conformations
    Yilun Du, Joshua Meier, Jerry Ma, Rob Fergus, Alexander W. Rives · 2020 · arXiv (Cornell University)
  54. A watershed moment for protein structure prediction
    Mohammed AlQuraishi · 2020 · Nature
  55. Deep learning methods for 3D structural proteome and interactome modeling
    Lee, Dongjin, Xiong, Dapeng, Wierbowski, Shayne, Li, Le, Liang, Siqi, Yu, Haiyuan · 2022 · Current Opinion in Structural Biology
  56. Advances in artificial intelligence-based technologies for increasing the quality of medical products
    Nidhi Srivastava, Sneha Verma, Anupama Singh, Pranki Shukla, Yashvardhan Singh, Ankit D. Oza, Tanvir Kaur, Sohini Chowdhury, Monit Kapoor, Ajar Nath Yadav · 2024 · DARU Journal of Pharmaceutical Sciences
  57. Machine Learning to Quantitate Neutrophil NETosis
    Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md Modasshir, Ioannis M. Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly et al. · 2019 · Scientific Reports
  58. tFold-Ab: Fast and Accurate Antibody Structure Prediction without Sequence Homologs
    Jiaxiang Wu, Fandi Wu, Biaobin Jiang, Wei Liu, Peilin Zhao · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
  59. PyUUL provides an interface between biological structures and deep learning algorithms
    Gabriele Orlando, Daniele Raimondi, Ramon Duran‐Romaña, Yves Moreau, Joost W. H. Schymkowitz, Frédéric Rousseau · 2022 · Nature Communications
  60. EncoderMap(II): Visualizing Important Molecular Motions with Improved Generation of Protein Conformations
    Tobias Lemke, Andrej Berg, Alok Jain, C. Peter · 2019 · Journal of Chemical Information and Modeling
  61. SidechainNet : An all‐atom protein structure dataset for machine learning
    Jonathan Edward King, David Ryan Koes · 2021 · Proteins Structure Function and Bioinformatics
  62. Multiscale modelling of drug mechanism and safety
    Jitao David Zhang, Lisa Sach-Peltason, Christian Krämer, Ken Wang, Martin Ebeling · 2019 · Drug Discovery Today
  63. The role of local versus nonlocal physicochemical restraints in determining protein native structure
    Skolnick, Jeffrey, Gao, Mu · 2021 · Current Opinion in Structural Biology
  64. Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction
    Ahmet G Genc, Liam James McGuffin · 2024 · Methods in molecular biology
  65. The parallelism motifs of genomic data analysis
    Katherine Yelick, Aydın Buluç, Muaaz Gul Awan, Ariful Azad, Benjamin Brock, Rob S Egan, Saliya Ekanayake, Marquita Ellis, Evangelos Georganas, Giulia Guidi et al. · 2020 · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
  66. Machine Learning at the Interface of Polymer Science and Biology: How Far Can We Go?
    Eleonora Gianti, Simona Percec · 2022 · Biomacromolecules
  67. PredictProtein – Predicting Protein Structure and Function for 29 Years
    Michael Bernhofer, Christian Dallago, Tim Karl, Venkata Satagopam, Michael Heinzinger, Maria Littmann, Tobias Olenyi, Jiajun Qiu, Konstantin Schütze, Guy Yachdav et al. · 2021 · bioRxiv (Cold Spring Harbor Laboratory)
  68. Learning neural generative dynamics for molecular conformation generation
    Minkai Xu · 2024 · TIB Data Manager
  69. Multimeric protein interaction and complex prediction: Structure, dynamics and function
    Da Lu, Shuhong Yu, Yixiang Huang, Xinqi Gong · 2025 · Computational and Structural Biotechnology Journal
  70. Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning
    Jack Hanson, Kuldip K. Paliwal, Thomas Litfin, Yuedong Yang, Yaoqi Zhou · 2019 · Journal of Computational Biology
  71. The Role of Protein Engineering in Biomedical Applications of Mammalian Synthetic Biology
    Daniel Bojar, Martin Fussenegger · 2019 · Small
  72. Transfer learning for predicting virus-host protein interactions for novel virus sequences
    Jack Lanchantin, Tom Weingarten, Arshdeep Sekhon, Clint L. Miller, Yanjun Qi · 2021 · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)
  73. Multilevel Chirality Transfer from Amino Acid Derivatives to Circularly Polarized Luminescence‐Active Nanoparticles in Aqueous Medium
    Yuan Chen, Baobao Sun, Haohui Feng, Ranran Wang, Ming Cheng, Peng Wang, Zhiping Zhou, Juli Jiang, Leyong Wang · 2021 · Chemistry - A European Journal
  74. Learning from nature by leveraging integrative biomateriomics modeling toward adaptive and functional materials
    Sofia E. Arevalo, Markus J. Buehler · 2023 · MRS Bulletin
  75. Limitations of Protein Structure Prediction Algorithms in Therapeutic Protein Development
    Sarfaraz K. Niazi, Zamara Mariam, Rehan Zafar Paracha · 2024 · BioMedInformatics
  76. Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure
    Nan Zhao, Tong Wu, Wenda Wang, Lunchuan Zhang, Xinqi Gong · 2024 · Interdisciplinary Sciences Computational Life Sciences
  77. Machine learning in biological research: key algorithms, applications, and future directions
    Md Nafis Ul Alam, Kiran Basava, Ani Chitransh, H. M. Abdul Fattah, Hector D. Garcia‐Verdugo, Shih-Hsuan Lo, Tanisha Lohchab, Kristen M. Martinet, Cristian Román‐Palacios, Jhan Carlos Salazar et al. · 2025 · BMC Biology
  78. Guidelines for designing peptoid structures: Insights from the Peptoid Data Bank
    James R. B. Eastwood, Ethan I. Weisberg, Dana Katz, Ronald N. Zuckermann, Kent Kirshenbaum · 2023 · Peptide Science
  79. Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review
    Farzan Soleymani, Eric Paquet, Herna Lydia Viktor, Wojtek Michalowski · 2024 · Computational and Structural Biotechnology Journal
  80. Computational Strategies for Scalable Genomics Analysis
    Shi, Lizhen, Wang, Zhong · 2019 · Genes
  81. Study of real-valued distance prediction for protein structure prediction with deep learning
    Jin Li, Jinbo Xu · 2021 · Bioinformatics
  82. Deep Learning and Site‐Specific Drug Delivery
    Dhanalekshmi Unnikrishnan Meenakshi, Nandakumar Selvasudha, Arul Prakash Francis, Joseph Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan, Shah Alam Khan · 2022 · Wiley eBooks
  83. Artificial intelligence for template-free protein structure prediction: a comprehensive review
    M. M. Mohamed Mufassirin, M. A. Hakim Newton, Abdul Sattar · 2022 · Artificial Intelligence Review
  84. Learning the shape of protein microenvironments with a holographic convolutional neural network
    Michael Neal Pun, Andrew Ivanov, Quinn Bellamy, Zachary Montague, Colin LaMont, Philip H Bradley, Jakub Otwinowski, Armita Nourmohammad · 2024 · Proceedings of the National Academy of Sciences
  85. Parallelized Natural Extension Reference Frame: Parallelized Conversion from Internal to Cartesian Coordinates
    Mohammed AlQuraishi · 2019 · Journal of Computational Chemistry
  86. Reduced B cell antigenicity of Omicron lowers host serologic response
    Jérôme Tubiana, Yufei Xiang, Li Fan, Haim J. Wolfson, Kong Chen, Dina Schneidman‐Duhovny, Yi Shi · 2022 · Cell Reports
  87. Deep exploration networks for rapid engineering of functional DNA sequences
    Johannes Linder, Nicholas Bogard, Alexander B. Rosenberg, Georg Seelig · 2019 · bioRxiv (Cold Spring Harbor Laboratory)
  88. Towards Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning
    Frederik Kratzert, Daniel Klotz, Mathew Herrnegger, Alden Keefe Sampson, Sepp Hochreiter, Grey Nearing · 2019
  89. Deep Generative Design of Epitope-Specific Binding Proteins by Latent Conformation Optimization
    Raphael R. Eguchi, Christian A. Choe, Udit Parekh, Irene S. Khalek, Michael D. Ward, Neha Vithani, Gregory R. Bowman, Joseph G. Jardine, Po‐Ssu Huang · 2022 · bioRxiv (Cold Spring Harbor Laboratory)
  90. Tectonic discrimination and application based on convolution neural network and incomplete big data
    Can Ge, Jinjing Huo, Hai‐Ou Gu, Fangyue Wang, He Sun, Xiuyu Li, Weiwei Li, Yuan Feng · 2020 · Journal of Geochemical Exploration
  91. Graph Neural Networks: a bibliometrics overview
    Abdalsamad Keramatfar, Mohadeseh Rafiee, Hossein Amirkhani · 2022 · Research Square
  92. Artificial intelligence-based methods for protein structure prediction: a survey
    Zhi‐Hui Zhan, Jun Hong, Jian-Yu Li, Cheng Wang, Langchong He, Zongben Xu, Jun Zhang · 2025 · Artificial Intelligence Review
  93. SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network
    Zhang, Xing, Yin, Hongmei, Ling, Fei, Zhan, Jian, Zhou, Yaoqi · 2023 · PLOS Computational Biology
  94. Structural biology meets data science: does anything change?
    Cameron Mura, Eli J. Draizen, Philip E. Bourne · 2018 · Current Opinion in Structural Biology
  95. Simple Model of Protein Energetics To Identify Ab Initio Folding Transitions from All-Atom MD Simulations of Proteins
    Massimiliano Meli, Giulia Morra, Giorgio Colombo · 2020 · Journal of Chemical Theory and Computation
  96. Deep learning-based digital volume correlation
    Xiaocen Duan, Jianyong Huang · 2022 · Extreme Mechanics Letters
  97. NCPepFold: Accurate Prediction of Noncanonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation
    Qingyi Mao, Tianfeng Shang, Wen Xu, Silong Zhai, Chengyun Zhang, Jingjing Guo, An Su, Chengxi Li, Hongliang Duan · 2025 · Journal of Chemical Theory and Computation
  98. RNANet: an automatically built dual-source dataset integrating homologous sequences and RNA structures
    Louis Becquey, Éric Angel, Fariza Tahi · 2020 · Bioinformatics
  99. TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks
    Esmaeil Nouranı, Ehsaneddin Asgari, Alice Carolyn McHardy, Mohammad R. K. Mofrad · 2021 · IEEE Transactions on Computational Biology and Bioinformatics
  100. Mapping enzyme-substrate interactions: its potential to study the mechanism of enzymes
    Sergi Rodà, Gerard Santiago, Vı́ctor Guallar · 2020 · Advances in protein chemistry and structural biology