ReviewInternational journal of molecular sciences2021
Deep Learning-Based Advances in Protein Structure Prediction.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.
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Who cites it
51 citing papers in PubMed.
- Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.Molecular diversity · 2026Review
- Navigating the uncharted: AI-driven advances in protein structure, dynamics, interactions and ligand interactions for understudied families.BioData mining · 2026Review
- Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision Nanomedicine.International journal of nanomedicine · 2026Review
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Perspectives: Comparison of deep learning segmentation models on biophysical and biomedical data.Biophysical journal · 2025Article
- DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations.BMC bioinformatics · 2025Article
- An outlook on structural biology after AlphaFold: tools, limits and perspectives.FEBS open bio · 2025Review
- Computational optimization of a pan-coronavirus fusion inhibitory peptide targeting spike's heptapeptide repeat region.Biosafety and health · 2025Article
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Artificial Intelligence (AI)-Based Protein Structure Prediction and Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.PLoS computational biology · 2024Article
- Template-based modeling of insect odorant receptors outperforms AlphaFold3 for ligand binding predictions.Scientific reports · 2024Article
- Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- iNP_ESM: Neuropeptide Identification Based on Evolutionary Scale Modeling and Unified Representation Embedding Features.International journal of molecular sciences · 2024Article
- Unraveling the metabolic potential of biocontrol fungi through omics data: a key to enhancing large-scaleapplication strategies.Acta biochimica et biophysica Sinica · 2024Review
- Enhanced therapeutic potential of antibody fragment via IEDDA-mediated site-specific albumin conjugation.Journal of biological engineering · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Obtaining an accurate description of protein structure is a fundamental step toward understanding the underpinning of biology. Although recent advances in experimental approaches have greatly enhanced our capabilities to experimentally determine protein structures, the gap between the number of protein sequences and known protein structures is ever increasing. Computational protein structure prediction is one of the ways to fill this gap. Recently, the protein structure prediction field has witnessed a lot of advances due to Deep Learning (DL)-based approaches as evidenced by the success of AlphaFold2 in the most recent Critical Assessment of protein Structure Prediction (CASP14). In this article, we highlight important milestones and progresses in the field of protein structure prediction due to DL-based methods as observed in CASP experiments. We describe advances in various steps of protein structure prediction pipeline viz. protein contact map prediction, protein distogram prediction, protein real-valued distance prediction, and Quality Assessment/refinement. We also highlight some end-to-end DL-based approaches for protein structure prediction approaches. Additionally, as there have been some recent DL-based advances in protein structure determination using Cryo-Electron (Cryo-EM) microscopy based, we also highlight some of the important progress in the field. Finally, we provide an outlook and possible future research directions for DL-based approaches in the protein structure prediction arena.
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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.