ReviewBiomarker research2025
Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.
Review in Biomarker research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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
22 citing papers in PubMed.
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Bispecific antibodies for cancer therapy: evolution of structural formats and co-targeting strategies from wet-lab to AI-driven in silico modeling.Cancer letters · 2026Review
- Article
- Landscape of T-cell bispecific antibodies in cancer therapy: therapeutic strategies, challenges and future prospection.Molecular cancer · 2026Review
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- Highly potent C-type nanoantibodies neutralize Nipah and Hendra viruses by cavity filling on fusion glycoprotein.Nature communications · 2026Article
- Enhancing persistence while managing cytokine release syndrome to embrace next-generation CAR-T cell therapy.Journal of translational medicine · 2026Review
- Isolation of neutralizing antibodies against SARS-CoV-2 through an epitope-guided negative screening by phage display.Journal of biomedical research · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- AI-driven nanomedicine for cancer theranostics.Molecular cancer · 2026Review
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Exploiting artificial intelligence in precision oncology: an updated comprehensive review.Journal of translational medicine · 2025Review
- Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- Technological advancements in antibody-based therapeutics for treatment of diseases.Journal of biomedical science · 2025Review
- Antibody-drug conjugates in cancer therapy: current landscape, challenges, and future directions.Molecular cancer · 2025Review
- Technologies for Monoclonal Antibody Discovery and Development.International journal of molecular sciences · 2025Review
- Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Targeting Aging Hallmarks with Monoclonal Antibodies: A New Era in Cancer Immunotherapy and Geriatric Medicine.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.
Indexed as
Identifiers
What OpenQuestion holds
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.