ArticleAntibody therapeutics2026
Generative AI-driven
Article in Antibody therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: CD93 is an emerging immune checkpoint. Blocking its interaction with insulin-like growth factor-binding protein-7 (IGFBP7) restores antitumor immunity, yet discovering high-affinity antibodies against this specific functional epitope remains a formidable challenge for traditional methods. Methods: We employed a proprietary generative artificial intelligence (AI) model, GeoFlow, to Results: To dissect the contribution of endothelial CD93 to immunotherapy resistance, we utilized an endothelial-specific conditional knockout model in MB49 bladder cancer, where the loss of endothelial CD93 significantly sensitized tumors to PD-1 inhibition. The genetic findings identified endothelial CD93 as a fundamental driver of immunotherapy resistance and provided the rationale for our GeoFlow generative AI platform, which was employed to Conclusions: This study validated generative AI for designing epitope-specific antibodies with superior potency, offering a robust candidate for next-generation immunotherapy.
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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.