Molecular surface descriptors to predict antibody developability: sensitivity to parameters, structure models, and conformational sampling.

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

19 citing papers in PubMed.

1. Predicting antibody self-association with sequence-structure fusion models: the central role of CSI-BLI in early developability screening.mAbs · 2026
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2. Structure-based calculation of excipient effects on the viscosity of concentrated antibody solutions.mAbs · 2026
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3. Machine learning predictions of IgG1 and IgG4 self-association and high-concentration solution properties.mAbs · 2026
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4. Self-interaction nanoparticle spectroscopy predicts high-concentration viscosity of therapeutic IgG1 antibodies.mAbs · 2026
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5. Multi-modal feature learning to prioritize ADCs with favorable half-life in mice.mAbs · 2026
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6. Local electrostatics governs tryptophan oxidation and enables rational stability engineering in antibodies.mAbs · 2026
   Article
7. Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026
   Review
8. Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026
   Article
9. Fab-Fc and Fab-Fab interactions of variable strength and valency contribute to the high concentration viscosity of IgGProceedings of the National Academy of Sciences of the United States of America · 2026
   Article
10. Insights into the structural differences between homo- and heterodimers enriched from a cocktail of monoclonal antibodies against SARS-CoV-2.Scientific reports · 2026
    Article
11. Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler.Communications biology · 2026
    Article
12. Optimizing colloidal stability and viscosity of multispecific antibodies at the drug discovery-development interface: a systematic predictive case study.mAbs · 2025
    Article
13. Accelerating antibody development: sequence and structure-based models for predicting developability properties via size exclusion chromatography.mAbs · 2025
    Article
14. Predicting purification process fit of monoclonal antibodies using machine learning.mAbs · 2025
    Article
15. PROPERMAB: an integrative framework formAbs · 2025
    Article
16. Mechanistic and predictive formulation development for viscosity mitigation of high-concentration biotherapeutics.mAbs · 2025
    Review
17. A real-time imaging approach to quantify dendritic cell internalization for immunogenicity risk assessment of biotherapeutics.Frontiers in immunology · 2025
    Article
18. Biophysical cartography of the native and human-engineered antibody landscapes quantifies the plasticity of antibody developability.Communications biology · 2024
    Article
19. Modulation of the high concentration viscosity of IgGmAbs
    Article

Corrections and comments

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

Authors and funding

2 authors.

Funding

No grant is acknowledged in the PubMed record.

Abstract

PubMed holds no abstract for this paper.

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