ArticlemicroPublication biology2026
Against All Odds: Computational Screening Via Machine Learning Ranking and Generation of Antibody Candidates for Creutzfeldt-Jakob Disease.
Article in microPublication biology, 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
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Authors and funding
2 authors.
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Abstract
Creutzfeldt-Jakob disease (CJD) is a fatal prion disorder with no approved treatments. This study developed a machine learning pipeline trained on 21 literature-curated PrP-targeting CDR sequences to rank 29,574 original and mutation-generated candidate sequences using neutralization, selectivity, and literature-based blood-brain barrier (BBB) proxy scores. The models showed internal performance (neutralization AUC = 0.9239; selectivity AUC = 0.7759), identified 10 high-percentile candidates, and generated 7,309 novel variants. These findings support hypothesis-generating sequence-level prioritization of PrP-targeting candidates, but do not establish native PrP
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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.