ArticlemAbs2025
AlphaBind, a domain-specific model to predict and optimize antibody-antigen binding affinity.
Article in mAbs, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- ASD: antigen-specific antibody database.mAbs · 2026Article
- A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation.iScience · 2026Article
- CLDN18.2 antibody design with protein language models: A deep learning optimization framework.PLoS computational biology · 2026Article
- The evolution of display technologies for antibody drug discovery.Trends in biotechnology · 2026Review
- The adaptive immune receptors in a big data world.ImmunoHorizons · 2026Review
- A Unified Dataset for Antibody and Nanobody Design Including Sequence, Structure, and Binding Affinity Data.Scientific data · 2026Article
- Article
- A Transformer-Based Deep Learning Approach to Predicting Air Organic Pollutant-Human Protein Interactions.Environmental science & technology · 2025Article
- Fine-tuned protein language model identifies antigen-specific B cell receptors from immune repertoires.bioRxiv : the preprint server for biology · 2025Article
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibodies are versatile therapeutic molecules that use combinatorial sequence diversity to cover a vast fitness landscape. Designing optimal antibody sequences, however, remains a major challenge. Recent advances in deep learning provide opportunities to address this challenge by learning sequence-function relationships to accurately predict fitness landscapes. These models enable efficient
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Identifiers
What OpenQuestion holds
Registered trials
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.