ArticleNature computational science2025
Investigating the volume and diversity of data needed for generalizable antibody-antigen ΔΔG prediction.
Article in Nature computational science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Investigating the effects of missense mutations on the dual Ras/Rap activation mechanism of SYNGAP1 using molecular modelling.Journal of structural biology: X · 2026Article
- ASD: antigen-specific antibody database.mAbs · 2026Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- A blinded, prospective benchmark of in silico antibody discovery anchored to experimental affinity and developability.Nature biotechnology · 2026Article
- Structure-based TCR-pMHC binding prediction and generalization to unseen peptides.npj drug discovery · 2026Article
- AbAgKer: a unified semi-supervised framework for antigen-antibody binding affinity and kinetics prediction.Bioinformatics (Oxford, England) · 2026Article
- Amplicon/Protein Bead Display enables quantitativebioRxiv : the preprint server for biology · 2026Article
- AbTune: layer-wise selective fine-tuning of protein language models for antibodies.Briefings in bioinformatics · 2026Article
- Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.Molecular cancer · 2026Review
- More protein-ligand data are needed for AlphaFold-like models to enable drug discovery.Current opinion in structural biology · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- MutPPI+: a multimodal framework for predicting mutation effects on protein-protein interactions via mutation-path-based data augmentation.Briefings in bioinformatics · 2026Article
- Ensemble molecular mimicry correlates with antibody cross-reactivity in proteome-wide studies.Frontiers in immunology · 2026Article
- Modeling antibody recognition across SARS-CoV-2 and SARS-CoV-1 using epitope-informed transfer learning.Frontiers in immunology · 2026Article
- Benchmarking antigen-aware inverse folding methods for antibody design.Bioinformatics advances · 2026Article
- Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.bioRxiv : the preprint server for biology · 2025Article
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- Graph attention with structural features improves the generalizability of identifying functional sequences at a protein interface.bioRxiv : the preprint server for biology · 2025Article
- Biophysics-based protein language models for protein engineering.Nature methods · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Antibody-antigen binding affinity lies at the heart of therapeutic antibody development: efficacy is guided by specific binding and control of affinity. Here we present Graphinity, an equivariant graph neural network architecture built directly from antibody-antigen structures that achieves test Pearson's correlations of up to 0.87 on experimental change in binding affinity (ΔΔG) prediction. However, our model, like previous methods, appears to be overtraining on the few hundred experimental data points available and performance is not robust to train-test cut-offs. To investigate the amount and type of data required to generalizably predict ΔΔG, we built synthetic datasets of nearly 1 million FoldX-generated and >20,000 Rosetta Flex ddG-generated ΔΔG values. Our results indicate that there are currently insufficient experimental data to accurately and robustly predict ΔΔG, with orders of magnitude more likely needed. Dataset size is not the only consideration; diversity is also an important factor for model predictiveness. These findings provide a lower bound on data requirements to inform future method development and data collection efforts.
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