Computational Antibody Papers
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2026
TitleKey points
2026-04-30
How to make the most of your masked language model for protein engineering
- protein design
- language models
- Method to select mutants computationally for lab testing.
- "Stochastic beam search," a sequence-centric method that evaluates masked language models (MLMs) via pseudo-log-likelihood, rather than using costly mutation-centric approaches.
- This technique is computationally efficient and produces higher-quality sequences by better balancing likelihood and diversity.
- The method was extensively validated through both in silico evaluations across various models and direct head-to-head in vitro antibody campaigns.
- In wet-lab testing, the optimized models effectively screened for synthesizability and binding, with supervised guidance achieving a 100% success rate in the experiments
- Implementation adjustments to Boltz-2 that make it run much faster.
- One of the biggest hurdles of running methods that use MSAs are MSA servers. They are computationally expensive and difficult to set up.
- MSA carries a lot of predictive value so skipping this step is unwise.
- Integrates MMseqs2-GPU directly into the Boltz-2 pipeline, removing the primary CPU bottleneck and enabling high-throughput, local structure prediction.
- This implementation streamlines the MSA process, making the predictions order of magnitude faster.
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
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.
- 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.
2026-03-20
Scaling Atomistic Protein Binder Design with Generative Pretraining And Test-time Compute
- protein design
- Novel generative framework to design protein binders from NVIDIA.
- Antibodies/nanobodie are not singled out for analysis.
- First framework to unify generative modeling with hallucination-based optimization, allowing for a strong generative prior to be steered by inference-time compute.
- The authors introduced Teddymer, a dataset of ~510,000 synthetic dimers created from AlphaFold predicted domain-domain interactions to overcome the scarcity of experimental multimer data.
- The model uses advanced search algorithms, including Beam Search, Feynman-Kac Steering, and MCTS, to navigate the generative space and find high-quality binders.
- It achieved state-of-the-art results on protein targets, small molecules, and enzyme design tasks, consistently outperforming baselines like RFDiffusion and BindCraft.
- No Wet-Lab testing. Hopefully just yet.
2026-03-20
Programming Biomolecular Interactions with All-Atom Generative Model
- nanobodies
- protein design
- AnewOmni, foundation model that unifies the design of small molecules, peptides, and antibodies into a single framework.
- The team evaluated approximately 3,000 candidates for the "undruggable" KRAS G12D target by alternating between AnewOmni for CDR design and AlphaFold3 for structural validation.
- Out of 7 synthesized nanobodies, the model achieved a 75% success rate (3 out of 4) when using a conservative structural consistency filter.
- The most successful nanobody design demonstrated a high binding affinity with a Kd of 587 nM
2026-02-05
Iterative Epitope Expansion Enables Structure-Guided de novo Design of IgE-Binding Miniproteins Targeting the FcεRI Interface
- non-antibody stuff
- protein design
- Describing a protocol to design mini-binders for a multi domain not that well characterized target using Latent-X1 and to lesser extent Chai.
- The protocol used Latent-X1 to generate de novo sequences and initial poses, which were then refolded using Chai-1 to ensure the designs were structurally consistent and plausible.
- The final rank was determined by the equation score = 2.0 * Binder PTM - 0.1 * min-iPAE - 0.1 * complex RMSD. This formula prioritized high global confidence (PTM) while penalizing designs where the Latent-X1 pose and Chai-1 refolded structure disagreed (iPAE and RMSD).
- To handle the complex, multidomain IgE interface, they first designed binders against a smaller, stable seed on the epsilon3 domain before iteratively expanding the interface toward the full receptor-binding site.
- Out of hundreds of generated designs, fewer than 80 candidates across two rounds were selected for wet-lab testing, resulting in a 6% hit rate and the identification of three specific IgE-binding miniproteins