Optimizing Data Volume and Diversity for Robust Antibody–Antigen ΔΔG Prediction

  • Volume Expansion: Hundreds of experimental ΔΔG measurements must scale to hundreds of thousands for reliable training.
  • Diversity Balancing: Sequence, structural, and mutation diversity prevents overfitting and enhances model generalization.
  • Synthetic Data Utility: Computationally generated ΔΔG values boost dataset size but need pairing with experimental diversity.
  • Embedding Strategies: Combining semantic embeddings with residue-level features improves sequence-only prediction accuracy.
  • Transfer Learning Leverage: Large-scale stability datasets inform binding ΔΔG models but hinge on structural-model fidelity.
  • Multitask Frameworks: Jointly predicting stability and affinity increases throughput and overall prediction robustness.
  1. Investigating the Volume and Diversity of Data Needed for Generalizable Antibody–Antigen ΔΔG Prediction: Hummer AM, et al., Nature Machine Intelligence: https://www.nature.com/articles/s43588-025-00823-8
  2. MVSF-AB: accurate antibody–antigen binding affinity prediction via multi-view sequence feature learning: Li M, et al., Bioinformatics: https://doi.org/10.1093/bioinformatics/btae579
  3. Transfer learning to leverage larger datasets for improved prediction of protein stability and protein–protein binding affinity: Meier D, et al., Proceedings of the National Academy of Sciences: https://www.pnas.org/doi/10.1073/pnas.2314853121
  4. AttABseq: an attention-based deep learning prediction method for antigen–antibody binding ΔΔG: Zhang Y, et al., Briefings in Bioinformatics: https://academic.oup.com/bib/article/25/4/bbae304/7705533
  5. Reliable prediction of protein–protein binding affinity changes upon mutations (Pythia-PPI): Zhao L, et al., PMC: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12199698/
  6. Recent advances in antibody optimization based on deep learning methods: Chen X, et al., Trends in Biotechnology: https://doi.org/10.1016/j.tibtech.2025.05.012

Achieving generalizable ΔΔG prediction for antibody–antigen interactions requires not only orders of magnitude more experimental data but also careful curation to maximize sequence, structural, and mutational diversity.

A cutting-edge ΔΔG predictor demands extensive and varied training examples. Recent work demonstrates that models like Graphinity, which leverages an equivariant graph neural network, hit performance ceilings when trained on only a few hundred experimental measurements; augmenting with nearly one million FoldX‐generated values improved correlation to 0.9 but exposed limitations in representativeness. Synthetic datasets can reveal how model accuracy scales with data size, yet without balanced representation of different antibody lineages, CDR variations, and mutation contexts, overfitting persists and generalization falters.

Sequence-based approaches such as MVSF-AB integrate semantic embeddings from proteinBERT and residue-level features, outperforming earlier methods on natural and mutant antigen–antibody pairs by mitigating structural-data scarcity through multi-view learning. However, their accuracy still hinges on training-set heterogeneity and may drop for novel antibody frameworks. Transfer learning from megascale stability datasets (e.g., ~400 000 reliable ΔΔG° points from protease sensitivity assays) has boosted model robustness for protein stability but reveals that structural fidelity of predicted models limits applicability to binding ΔΔG tasks.

Attention-based sequence predictors like AttABseq further highlight that diverse mutation landscapes—single and multi-point changes across >32 complexes—are essential to sustain prediction stability (PCC up to 0.587) and prevent performance plateaus seen with homogeneous datasets. Multitask frameworks (e.g., Pythia-PPI) trained on augmented SKEMPI-like interfaces (~400 000 mutations) illustrate that integrating protein stability and binding-affinity predictions can increase throughput (>10 000 predictions/min) while maintaining high correlation (r=0.785).

Ultimately, experimental ΔΔG measurements spanning multiple logs of affinity and encompassing diverse antibody scaffolds, antigen epitopes, and mutation types remain critical. Strategic dataset expansion—balancing structure-derived, sequence-based, and synthetic entries—emerges as the key to unlocking truly generalizable ΔΔG prediction for therapeutic antibody engineering.