ArticleNature communications2024
Pretrainable geometric graph neural network for antibody affinity maturation.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 27 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
27 citing papers in PubMed.
- Article
- Review
- PreFold-dG: estimating binding affinity of protein-protein interaction from intermediate representations of protein folding model.Bioinformatics (Oxford, England) · 2026Article
- Generative AI-drivenAntibody therapeutics · 2026Article
- USP-ddG: a unified structural paradigm with data efficacy and mixture-of-experts for predicting mutational effects on protein-protein interactions.Bioinformatics (Oxford, England) · 2026Article
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Article
- Repertoire-scale antibody structural prediction informs therapeutic design.Science advances · 2026Article
- Structural Basis of Serine Protease Inhibition by Antibodies from Biased Fab Phage-Display Libraries.bioRxiv : the preprint server for biology · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- From Latent Manifolds to Targeted Molecular Probes: An Interpretable, Kinome-Scale Generative Machine Learning Framework for Family-Based Kinase Ligand Design.Biomolecules · 2026Article
- Inhibition of LONP1 in prostate cancer: bibliometrics-guided target screening and AI-driven antibody design.International journal of surgery (London, England) · 2026Article
- Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.Military Medical Research · 2026Review
- Explore antibody repertoire in the era of AI.Acta biochimica et biophysica Sinica · 2025Article
- Artificial intelligence-driven computational methods for antibody design and optimization.mAbs · 2025Review
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- A synergistic generative-ranking framework for tailored design of therapeutic single-domain antibodies.Cell discovery · 2025Article
- CATH-ddG: towards robust mutation effect prediction on protein-protein interactions out of CATH homologous superfamily.Bioinformatics (Oxford, England) · 2025Article
- Nanobody based immunoassay for detection of aquatic virus: Giant salamander iridovirus.Analytica chimica acta · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
Increasing the binding affinity of an antibody to its target antigen is a crucial task in antibody therapeutics development. This paper presents a pretrainable geometric graph neural network, GearBind, and explores its potential in in silico affinity maturation. Leveraging multi-relational graph construction, multi-level geometric message passing and contrastive pretraining on mass-scale, unlabeled protein structural data, GearBind outperforms previous state-of-the-art approaches on SKEMPI and an independent test set. A powerful ensemble model based on GearBind is then derived and used to successfully enhance the binding of two antibodies with distinct formats and target antigens. ELISA EC
Indexed as
Identifiers
What Socratic 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 Socratic graph.