ReviewActa pharmacologica Sinica2025
AI-driven antibody design with generative diffusion models: current insights and future directions.
Review in Acta pharmacologica Sinica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Mixture diffusion model for multimodal antibody design.Briefings in bioinformatics · 2026Article
- Monoclonal Antibodies Targeting Bacterial Infections: A Broad Review of the Field.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2026Review
- Highly potent C-type nanoantibodies neutralize Nipah and Hendra viruses by cavity filling on fusion glycoprotein.Nature communications · 2026Article
- Harnessing deep learning to accelerate the development of antibodies and aptamers.Acta pharmaceutica Sinica. B · 2026Review
- Physics-Informed Artificial Intelligence Design of Picomolar Nanobodies Enables Deep Tumor Penetration and High-Contrast Imaging.Research (Washington, D.C.) · 2026Article
- Computational Design of Broad-Spectrum Ebola Antibodies through Framework and Complementarity-Determining Region Synergistic Optimization.Research (Washington, D.C.) · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.Military Medical Research · 2026Review
- De novo design of epitope-specific antibodies via a structure-driven computational workflow.Nature communications · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Review
- Review
- Structures of CRP antigen-antibody complexes provide insights into the mechanism of specific recognition.Communications biology · 2025Article
- An iterative strategy to design 4-1BB agonist nanobodies de novo with generative AI models.Scientific reports · 2025Article
- Artificial intelligence-, organoid-, and organ-on-chip-powered models to improve pre-clinical animal testing of vaccines and immunotherapeutics: potential, progress, and challenges.Frontiers in artificial intelligence · 2025Review
- AI-based antibody design targeting recent H5N1 avian influenza strains.Computational and structural biotechnology journal · 2025Article
- The Application of Machine Learning on Antibody Discovery and Optimization.Molecules (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Therapeutic antibodies are at the forefront of biotherapeutics, valued for their high target specificity and binding affinity. Despite their potential, optimizing antibodies for superior efficacy presents significant challenges in both monetary and time costs. Recent strides in computational and artificial intelligence (AI), especially generative diffusion models, have begun to address these challenges, offering novel approaches for antibody design. This review delves into specific diffusion-based generative methodologies tailored for antibody design tasks, de novo antibody design, and optimization of complementarity-determining region (CDR) loops, along with their evaluation metrics. We aim to provide an exhaustive overview of this burgeoning field, making it an essential resource for leveraging diffusion-based generative models in antibody design endeavors.
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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.