ArticleCell systems2019
End-to-End Differentiable Learning of Protein Structure.
Article in Cell systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 140 papers.
What it found
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Who cites it
140 citing papers in PubMed.
- Integrating evolutionary and compositional features with ML and DL for robust and interpretable druggable protein prediction.Journal of computer-aided molecular design · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- From CASP13 to the Nobel Prize: DeepMind's AlphaFold Journey in Revolutionizing Protein Structure Prediction and Beyond.Current protein & peptide science · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- Machine learning in biological research: key algorithms, applications, and future directions.BMC biology · 2025Review
- Identifying Cortical Molecular Biomarkers Potentially Associated with Learning in Mice Using Artificial Intelligence.International journal of molecular sciences · 2025Article
- Chemosensory Receptors in Vertebrates: Structure and Computational Modeling Insights.International journal of molecular sciences · 2025Review
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Multimeric protein interaction and complex prediction: Structure, dynamics and function.Computational and structural biotechnology journal · 2025Review
- Connecting the dots: deep learning-based automated model building methods in cryo-EM.Frontiers in molecular biosciences · 2025Review
- How the technologies behind self-driving cars, social networks, ChatGPT, and DALL-E2 are changing structural biology.BioEssays : news and reviews in molecular, cellular and developmental biology · 2025Review
- Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- MCNN_MC: Computational Prediction of Mitochondrial Carriers and Investigation of Bongkrekic Acid Toxicity Using Protein Language Models and Convolutional Neural Networks.Journal of chemical information and modeling · 2024Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Advances in artificial intelligence-based technologies for increasing the quality of medical products.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Review
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Review and Comparative Analysis of Methods and Advancements in Predicting Protein Complex Structure.Interdisciplinary sciences, computational life sciences · 2024Review
- On the role of native contact cooperativity in protein folding.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deep learning that replace complex, human-designed pipelines with differentiable models optimized end to end suggest the potential benefits of similarly reformulating structure prediction. Here, we introduce an end-to-end differentiable model for protein structure learning. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co-evolutionary data and predicting known folds without structural templates. In the first task, the model achieves state-of-the-art accuracy, and in the second, it comes within 1-2 Å; competing methods using co-evolution and experimental templates have been refined over many years, and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.