Computational Antibody Papers
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2026-04-24
BOAT: Navigating the Sea of In Silico Predictors for Antibody Design via Multi-Objective Bayesian Optimization
- protein design
- developability
- Case study & framework how to tie together available computational annotators to perform cross reactivity optimization for a VHH.
- It replaces inefficient, sequential screening pipelines with a multi-objective Bayesian optimization loop. It uses a Gaussian process surrogate model coupled with a genetic algorithm to navigate complex sequence spaces and identify Pareto-optimal candidates.
- The framework is model-agnostic; users must provide and validate the in silico "oracles" (predictive models) relevant to their specific optimization goals. Objectives are defined by selecting and potentially weighting these interchangeable scoring functions.
- The authors rigorously benchmarked BOAT against standard genetic algorithms and generative baselines (like LaMBO-2). Testing relied on computational benchmarks, including comparing results against exhaustive "ground truth" Pareto fronts in limited search spaces.
- The study did not perform wetlab validation. Because the framework relies entirely on in silico oracles as proxies, the final experimental success of the optimized candidates is ultimately tied to the predictive quality of the models the user selects.
2026-04-24
DualGPT-AB: a dual-stage generative optimization framework for therapeutic antibody design
- binding prediction
- Case study application of generation of novel HER2 binders using the Herceptin template, with five specific computational properties (HER2 specificity, FvNetCharge, FvCSP, HISum, and MHC II minPR) encoded as constraints.
- The authors train a conditional CDRH3 GPT (based on a mini GPT-2 architecture) using large-scale sequences sourced from the OAS database.
- Sequences are computationally annotated with property labels and refined via reinforcement learning (RL) to satisfy multi-property constraints.
- Target-specific binding predictors (oracles) are used to guide the RL process to generate CDRH3 sequences that exhibit HER2-targeting capabilities similar to Herceptin.
- Wet-lab validation confirms HER2-binding affinity and tumoricidal efficacy; while physical developability assays were not performed in the lab, these traits were primary objectives of the computational design stage.
2026-04-24
Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design
- protein design
- Protein design model applied to antibodies and lab-tested.
- Protenix-v2 is an integrated biomolecular modeling system that enables high-accuracy structure prediction, zero-shot generative binder design, and improved ligand-related plausibility.
- The system incorporates refined architecture and training optimizations, while strictly excluding all wwPDB entries released on or after September 30, 2021, to prevent data leakage.
- Performance was assessed using DockQ success rates on antibody-antigen interface benchmarks, BLI-confirmed hit rates across diverse soluble and membrane-protein targets, and PoseBusters-style chemical validity metrics
2026-04-17
Agent-guided de novo design of nanobody binders against a novel cancer target
- agents
- protein design
- Workflow for de novo nanobody design: Establishing an integrated computational-experimental pipeline for single-domain antibody discovery.
- Selected a novel target for Desmoplastic Small Round Cell Tumor (DSRCT) with no prior structural or antibody data.
- Used an AI agent to synthesize bioinformatics tool outputs and recommend 8 binding hotspots.
- Employed RFantibody, mBER, and IgGM to generate 288,000 unique candidates.
- Nominated 100,000 designs via Pareto-based filtering for yeast surface display and FACS enrichment.
- 116 enriched candidates were characterized by SPR, yielding 46 confirmed binders (39.7% hit rate) with affinities as low as 0.66 nM.
2026-04-17
Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design
- developability
- Training of a baseline developability predictor on the Gingko dataset.
- Utilized the GDPal benchmark from Ginkgo Bioworks, consisting of 242 therapeutic IgGs across five assays: HIC, AC-SINS, PR_CHO, Titer, and Tm2.
- Employed frozen ESM-Cambrian encoders (up to 6B parameters) to generate embeddings, which were processed by property-specific attention decoders (Self, Self+Cross, or Bidirectional Cross) and a prediction head.
- Achieved significant improvements over baselines in 3/5 properties: expression titer (+20%), thermal stability (+18%), and polyreactivity (+12%).
- Optimal attention schemes differ by property; self-attention alone suffices for aggregation-related traits (HIC, PR_CHO), while bidirectional cross-attention is required for properties involving inter-chain compatibility (Titer, Tm2).
2026-04-17
AbDist: a lightweight, distance-based model for antibody affinity prediction as an interpretable benchmark for machine learning models
- binding prediction
- Very lightweight method to predict binding affinity of antibody-antigen complexes.
- Local sequence fragments of length 2r+1 are extracted around mutation sites. Distances between these fragments are calculated using the Levenshtein distance to account for sequence shifts.
- Targets are predicted using a k-nearest neighbors (kNN) approach (regression or classification) based on the closest matching fragments in the training set.
- Despite its simplicity, the model achieves results comparable to state-of-the-art machine learning models on datasets like AB-Bind, AbDesign, and Alphaseq.
- It serves as an interpretable benchmark particularly suited for data-sparse, target-specific antibody engineering where experimental data is limited.
2026-04-17
Sampling antibody conformational ensembles with ABodyBuilder4-STEROIDS
- structure prediction
- Fourth version of ABodyBuilder: Now uses a generative flow matching model (ABB4-STEROIDS) specifically designed to sample antibody conformational ensembles.
- Trained on FlAbDab and all-atom MD, utilizes 4.2 million frames from ~136,000 coarse-grained simulations, plus a new fine-tuning set of 83 all-atom MD simulations.
- Shows marginal improvements over ABB3 but is less accurate than Boltz-1 for single-structure RMSD; it also exhibits a higher number of atomic clashes than Boltz-1.
- Outperforms alternative models (including Boltz-1 and AlphaFlow) at reproducing MD ensemble metrics and matching experimental evidence of CDR loop diversity.
- Measuring the effects of protein energetics versus actual protein-protein binding.
- The authors used AlphaSeq to measure 7,185 single and double mutations across four VHH-antigen complexes to capture changes in observed affinity.
- By using "control VHHs" that bind to non-overlapping epitopes, they successfully separated "protein-quality" (folding/stability) effects from true "protein-interaction" (interface) changes.
- The study found that 83.6% - 98.9% of antigen mutations negatively impact affinity, primarily by degrading the protein's overall quality rather than disrupting specific interface energetics.
- Benchmarking showed that sota models like ESM-IF1 and ThermoMPNN are effective at predicting protein-quality changes but struggle to accurately predict specific protein-interaction effects.
2026-04-03
Evaluating Deep Learning Based Structure Prediction Methods on Antibody-Antigen Complexes
- structure prediction
- Authors benchmark co-folding methods on their ability to identify true positives given deep sampling.
- AlphaFold3 consistently outperforms AlphaFold2, Chai-1, and Boltz-1 in predicting antibody-antigen complexes, though its accuracy declines if the target lacks structural similarity to its training data.
- For all methods, increased sampling improves the probability of generating a correct model in a roughly log-linear manner; however, the improvement is limited by a significant gap between the "best" model generated and the "top-ranked" model.
- Internal confidence metrics (like ipTM) struggle to identify the most accurate structures for a given target, primarily because the models cannot yet accurately predict their own aligned errors.
2026-04-03
AbAffinity: A Large Language Model for Predicting Antibody Binding Affinity against SARS-CoV-2
- binding prediction
- Case study of training of an affinity prediction algorithm on anti-sars-cov-2 antibodies.
- Authors fine-tuned a BERT-based model, Ab-Affinity, specifically to predict the binding affinity of antibodies against the SARS-CoV-2 spike protein.
- They utilized a dataset of 71,834 unique antibodies (preprocessed from 104,972 variants) derived from three parental "seed" binders with experimentally measured affinities.
- The model employs a BERT-based encoder (specifically ESM-2) with an added fully connected regression layer to predict continuous binding scores.
- Ab-Affinity achieved higher Pearson and Spearman correlation coefficients on the test set than existing LLM-based methods like DG-Affinity, ESM-2, and AbLang. But the baselines were not fine tuned on their data.