ReviewNPJ precision oncology2025
Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.
Review in NPJ precision oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- Antibody-drug conjugate engineering: from design to efficacy and safety.Signal transduction and targeted therapy · 2026Review
- Payload-Based Clinical Pharmacology Review of Approved Antibody-Drug Conjugates: Commonalities and Considerations for Streamlined Development.Clinical pharmacology and therapeutics · 2026Review
- Circulating tumour cells for guiding antibody‒drug conjugate therapy: Role of artificial intelligence.Clinical and translational medicine · 2026Review
- From Unmet Medical Need to Drug Candidate: A Translational Therapeutic Development Roadmap Illustrated by Dual-Payload Antibody-Drug Conjugates.Biomolecules · 2026Review
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Advances and Future Directions in Antibody-Drug Conjugates: From Paradigm Shifts to Data-Driven Design.Cancers · 2026Review
- Article
- Understanding and Overcoming Antibody-Drug Conjugate Resistance: Biological Mechanisms and Emerging Analytical Frameworks in Breast Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Challenges and Opportunities for Cleavable Linkers Used in Polymer-Drug Conjugates.Journal of the American Chemical Society · 2026Review
- Next-Generation Artificial Intelligence Strategies for Mechanistic Cancer Target Discovery and Drug Development: A State-of-the-Art Review.International journal of molecular sciences · 2026Review
- Evolving antibody-drug conjugates in breast cancer from precision delivery to tumor microenvironment reprogramming.Journal of hematology & oncology · 2026Review
- Antibody-Drug Conjugates Reshape the Landscape of Cancer Therapy: Evolution, Challenges and Future Perspectives from the Concept of "Magic Bullet" to Clinical Application.Current oncology reports · 2026Review
- Antibody-Drug Conjugates in Oncology: Principles, Clinical Development, and Future Directions.MedComm · 2026Review
- Review
- Toward adaptive therapeutic timing: integration of mechanistic pharmacology and artificial intelligence in precision dosing.Frontiers in pharmacology · 2026Review
- Precision immuno-oncology in NSCLC: integrating ADCs, therapeutic vaccines, and adoptive cell therapies for next-generation systemic treatment.Frontiers in oncology · 2026Article
- Engineering the Future of ADCs in Non-Small Cell Lung Cancer.Oncology research · 2026Review
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
Abstract
Artificial intelligence (AI) is opening new frontiers in the development of antibody-drug conjugates (ADCs), offering unprecedented opportunities for precision therapy. This review outlines how AI empowers each stage of the ADC pipeline. In target discovery, multi-omics integration and graph-based learning prioritize tumor-selective and internalizing antigens. In antibody engineering, structure prediction, affinity optimization, and developability modeling streamline candidate selection. For linker-payload design, generative models and multi-objective optimization approaches support the rational design of conjugates that balance potency, stability, and immunogenicity. In absorption, distribution, metabolism, excretion, and toxicity (ADMET) modeling, deep learning and transformer-based frameworks predict pharmacokinetics and toxicity with increasing accuracy and mechanistic clarity. In clinical development, AI facilitates patient stratification, response prediction, and trial simulation through digital twin models, adaptive dosing algorithms, and real-world data integration. These capabilities support a more personalized and efficient pathway from bench to bedside. To further realize the impact of AI in ADC development, we highlight strategic priorities including the creation of curated, multimodal datasets, interpretable model architectures, and closed-loop experimental platforms. Together, these advances will be essential for realizing the full potential of AI to support rational, scalable, and personalized ADC-based therapies in oncology.
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.