ReviewMolecular systems biology2024
Deep learning for protein structure prediction and design-progress and applications.
Review in Molecular systems biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 46 citations in OpenAlex.
- Pockets to products: a data-driven approach for classification of monoterpene synthases.Digital discovery · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Stoic: fast and accurate protein stoichiometry prediction.Bioinformatics (Oxford, England) · 2026Article
- Fungal Carbonic Anhydrases: A Systematic Review from Molecular Profiling to Pathogenic Regulation inJournal of fungi (Basel, Switzerland) · 2026Review
- Artificial intelligence in biologic drug discovery: A review of methodological evolution and therapeutic applications.Acta pharmaceutica Sinica. B · 2026Review
- Predictions from deep learning propose substantial protein-carbohydrate interplay.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Machine Learning-Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments.Journal of chemical information and modeling · 2026Article
- Protein contact network explorer: topological analysis of protein structures.Frontiers in bioinformatics · 2026Article
- Adaptive deletion of functional duplicate genes inbioRxiv : the preprint server for biology · 2025Article
- Deep learning-assisted tools to understand the structural biology of the synapse.Biomedical engineering letters · 2025Review
- Ballistic Microscopy (BaM).bioRxiv : the preprint server for biology · 2025Article
- From Bench to Bedside: Emerging Paradigms in CAR-T Cell Therapy for Solid Malignancies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- FIBOS: R and python packages for analyzing protein packing and structure.Bioinformatics (Oxford, England) · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- Population health management fit lifecycles in analytics.Frontiers in artificial intelligence · 2025Article
- Integration of co-culture and transport engineering for enhanced metabolite production.Plant biotechnology (Tokyo, Japan) · 2024Article
- Allostery Illuminated: Harnessing AI and Machine Learning for Drug Discovery.ACS medicinal chemistry letters · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proteins are the key molecular machines that orchestrate all biological processes of the cell. Most proteins fold into three-dimensional shapes that are critical for their function. Studying the 3D shape of proteins can inform us of the mechanisms that underlie biological processes in living cells and can have practical applications in the study of disease mutations or the discovery of novel drug treatments. Here, we review the progress made in sequence-based prediction of protein structures with a focus on applications that go beyond the prediction of single monomer structures. This includes the application of deep learning methods for the prediction of structures of protein complexes, different conformations, the evolution of protein structures and the application of these methods to protein design. These developments create new opportunities for research that will have impact across many areas of biomedical research.
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What OpenQuestion holds
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.