Computational Antibody Papers
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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.
- Evedesign, an open-source, method-agnostic framework that standardizes biosequence design by enabling different machine learning models (sequence, structure, and evolutionary) to work together in a single workflow.
- It works by framing design as a conditional modeling problem using three composable operations: Generate (creating new sequences), Score (predicting fitness or likelihood), and Transform (mapping between representations like sequence-to-structure).
- The authors did not perform new wet-lab experiments; instead, they tested the framework by computationally reproducing previous studies, showing ESM-2 and ProteinMPNN could successfully rank and prioritize known beneficial mutations from existing antibody datasets.
2026-03-27
Latent-Y: A Lab-Validated Autonomous Agent for De Novo Drug Design
- nanobodies
- protein design
- agents
- First autonomous nanobody design agent.
- Prompted by high-level goals: It translates natural language objectives, like "inhibit X interaction with Y", into complete design campaigns.
- The agent queries literature/databases, uses bioinformatics tools, and prompts the user for specific strategic clarifications.
- 56x expert-level speedup, by compressing weeks of expert research and computational tasks into hours by automating reasoning-intensive steps.
- In lab tests, it successfully generated functional binders for 6 out of 9 attempted targets.
- Authors advocate for a "prompt-to-drug" autonomous pipeline, using a central AI orchestrator to connect disparate pre-clinical and clinical steps agentically.
- While modular proofs-of-concept exist, they remain domain-specific, brittle, and far from full-cycle implementation in actual drug discovery programs.
- A primary recommendation is to eliminate "data silos" by making research open, peer-reviewed, and accessible via APIs to ensure outputs are easily "machine-readable" for AI training.
- The system faces significant hurdles from LLM hallucinations and "cascading errors," where a single early-stage miscalculation (like an incorrect binding pocket) propagates through the entire chain.
- Despite the push for autonomy, authors argue "human-in-the-loop" checkpoints remain legally and ethically mandatory for high-stakes regulatory and clinical transitions.