Evidence map›Paper›PMID 42665538›Full record

ArticlemAbs2026

DyAb: sequence-based antibody design and property prediction in a low-data regime.

Joshua Yao-Yu Lin, Jennifer L Hofmann, Andrew Leaver-Fay, Wei-Ching Liang, Stefania Vasilaki, Edith Lee, Pedro O Pinheiro, Natasa Tagasovska, James R Kiefer, Yan Wu and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in mAbs, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Joshua Yao-Yu LinPrescient Design, Genentech, South San Francisco, CA, USA.
Jennifer L HofmannPrescient Design, Genentech, South San Francisco, CA, USA.
Andrew Leaver-FayPrescient Design, Genentech, South San Francisco, CA, USA.
Wei-Ching LiangDepartment of Antibody Engineering, Genentech, South San Francisco, CA, USA.
Stefania VasilakiPrescient Design, Genentech, South San Francisco, CA, USA.
Edith LeePrescient Design, Genentech, South San Francisco, CA, USA.
Pedro O PinheiroPrescient Design, Genentech, South San Francisco, CA, USA.
Natasa TagasovskaPrescient Design, Genentech, South San Francisco, CA, USA.
James R KieferDepartment of Structural Biology, Genentech, South San Francisco, CA, USA.
Yan WuDepartment of Antibody Engineering, Genentech, South San Francisco, CA, USA.
Franziska SeegerPrescient Design, Genentech, South San Francisco, CA, USA.
Richard BonneauPrescient Design, Genentech, South San Francisco, CA, USA.
Vladimir GligorijevicPrescient Design, Genentech, South San Francisco, CA, USA.
Andrew WatkinsPrescient Design, Genentech, South San Francisco, CA, USA.
Kyunghyun ChoPrescient Design, Genentech, South San Francisco, CA, USA.
Nathan FreyPrescient Design, Genentech, South San Francisco, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in binding affinity, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates antibody variants with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for optimizing antibody binding affinity in low data regimes common in early-stage drug development.

Indexed as

Antibodies, MonoclonalDrug DesignProtein EngineeringAlgorithmsAntibody AffinityGenetic AlgorithmsHumansMachine LearningPrediction AlgorithmsAntibodies, MonoclonalAffinity maturationantibody engineeringmachine learning

Identifiers

PMID42665538
PMCPMC13531962

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.