ReviewFrontiers in pharmacology2026
Antibody-enabled structural biology and AI-driven antibody design.
Review in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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0 citing papers in PubMed.
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Authors and funding
2 authors.
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Abstract
Membrane proteins govern essential cellular processes, including ion transport, signal transduction, and molecular recognition, and collectively represent more than half of all current therapeutic targets. Yet their structural characterization remains challenging due to intrinsic instability, amphipathic surfaces, and conformational heterogeneity. Over the past decade, antibody-based approaches, spanning full-length immunoglobulins, antigen-binding fragments (Fabs), nanobodies, and engineered scaffolds such as designed ankyrin repeat proteins (DARPins), have transformed structural biology by stabilizing dynamic states, augmenting molecular weight for cryo-electron microscopy (cryo-EM), and enabling visualization of previously inaccessible complexes. In parallel, advances in artificial intelligence and machine learning have begun to enhance predictive modeling, accelerate structure determination, and guide rational design of protein-ligand and antibody-antigen interactions. This review examines how antibody engineering and AI-driven computation together are reshaping the landscape of structural biology and therapeutic discovery.
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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.