ReviewComputational and structural biotechnology journal2022
Protein-protein interaction prediction with deep learning: A comprehensive review.
Review in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 80 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
80 citing papers in PubMed.
- Article
- SoPPIs: a highly parallelized protein-protein-interaction screening method in prokaryotic and eukaryotic hosts.Nucleic acids research · 2026Article
- Vitiligo Information Resource database v3.NPJ systems biology and applications · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Review
- The protein-protein interaction network analysis in idiopathic posterior uveitis.Indian journal of ophthalmology · 2026Article
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- Multimodal graph, surface, and language-based model for protein protein interaction prediction.Scientific reports · 2026Article
- Prediction of protein-protein interactions using point transformer and spherical Convex Hull graphs.Computational and structural biotechnology journal · 2026Article
- HVIface: sequence-based deep learning for decoding human-virus protein-protein interfaces.Frontiers in bioinformatics · 2026Article
- Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.Chemistry & biodiversity · 2026Article
- Interactomics: Dissecting Protein Networks in Living Systems.Advances in experimental medicine and biology · 2026Review
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Improving protein interaction prediction in GenPPi: a novel interaction sampling approach preserving network topology.BMC bioinformatics · 2025Article
- Benchmarking pre-trained genomic language models for RNA sequence-related predictive applications.Nature communications · 2025Article
- Towards a phylogenetically informed approach to solving protein-protein interactions.Biochemical Society transactions · 2025Review
- EGCPPIS: learning hierarchical equivariant graph representations with contrastive integration for protein-protein interaction site identification.BMC bioinformatics · 2025Article
- Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery.RSC medicinal chemistry · 2025Review
- DCMF-PPI: a protein-protein interaction predictor based on dynamic condition and multi-feature fusion.BMC bioinformatics · 2025Article
20 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most proteins perform their biological function by interacting with themselves or other molecules. Thus, one may obtain biological insights into protein functions, disease prevalence, and therapy development by identifying protein-protein interactions (PPI). However, finding the interacting and non-interacting protein pairs through experimental approaches is labour-intensive and time-consuming, owing to the variety of proteins. Hence, protein-protein interaction and protein-ligand binding problems have drawn attention in the fields of bioinformatics and computer-aided drug discovery. Deep learning methods paved the way for scientists to predict the 3-D structure of proteins from genomes, predict the functions and attributes of a protein, and modify and design new proteins to provide desired functions. This review focuses on recent deep learning methods applied to problems including predicting protein functions, protein-protein interaction and their sites, protein-ligand binding, and protein design.
Indexed as
Identifiers
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.