ReviewComputational and structural biotechnology journal2020
Deep learning methods in protein structure prediction.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
90 citing papers in PubMed, 1 synthesis or guideline pooled it.
- In Silico Approaches Targeting Quorum-Sensing Inhibition inMolecules (Basel, Switzerland) · 2026Pooled it
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- An overview of self-supervised deep learning applications to molecular data.Briefings in bioinformatics · 2026Review
- Benchmarking Generative AI Protein Models Reveals Differences Between Structural and Sequence-based Approaches.Genomics, proteomics & bioinformatics · 2026Article
- Enhancing minority class recovery in high resolution land cover mapping with dynamic imbalance aware oversampling.Scientific reports · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- Human β defensin type 2 based novel antimicrobial peptide prediction using both molecular dynamics simulation and artificial intelligence methods.Frontiers in chemistry · 2026Article
- AT-TSVM: Improving Transmembrane Protein Inter-Helical Residue Contact Prediction Using Active Transfer Transductive Support Vector Machines.International journal of molecular sciences · 2025Article
- Review
- Towards Post-Genomic Oncology: Embracing Cancer Complexity via Artificial Intelligence, Multi-Targeted Therapeutics, Drug Repurposing, and Innovative Study Designs.International journal of molecular sciences · 2025Review
- MEN: leveraging explainable multimodal encoding network for precision prediction of CYP450 inhibitors.Scientific reports · 2025Article
- Adaptive gradient scaling: integrating Adam and landscape modification for protein structure prediction.BMC bioinformatics · 2025Article
- Unknotting RNA: A method to resolve computational artifacts.PLoS computational biology · 2025Article
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- ResUbiNet: A Novel Deep Learning Architecture for Ubiquitination Site Prediction.Current genomics · 2025Article
- Advancements in one-dimensional protein structure prediction using machine learning and deep learning.Computational and structural biotechnology journal · 2025Review
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- ET-PROTACs: modeling ternary complex interactions using cross-modal learning and ternary attention for accurate PROTAC-induced degradation prediction.Briefings in bioinformatics · 2024Article
- MHC-Fine: Fine-tuned AlphaFold for precise MHC-peptide complex prediction.Biophysical journal · 2024Article
30 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein Structure Prediction is a central topic in Structural Bioinformatics. Since the '60s statistical methods, followed by increasingly complex Machine Learning and recently Deep Learning methods, have been employed to predict protein structural information at various levels of detail. In this review, we briefly introduce the problem of protein structure prediction and essential elements of Deep Learning (such as Convolutional Neural Networks, Recurrent Neural Networks and basic feed-forward Neural Networks they are founded on), after which we discuss the evolution of predictive methods for one-dimensional and two-dimensional Protein Structure Annotations, from the simple statistical methods of the early days, to the computationally intensive highly-sophisticated Deep Learning algorithms of the last decade. In the process, we review the growth of the databases these algorithms are based on, and how this has impacted our ability to leverage knowledge about evolution and co-evolution to achieve improved predictions. We conclude this review outlining the current role of Deep Learning techniques within the wider pipelines to predict protein structures and trying to anticipate what challenges and opportunities may arise next.
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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.