ReviewJournal of biomedical science2025
Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.
Review in Journal of biomedical science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Trial Watch - bispecific T cell engagers and higher-order multispecific immunotherapeutics.Oncoimmunology · 2026Review
- Review
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 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
- From Single Cells to Silicon: Emerging Technologies Transforming Monoclonal Antibody Discovery.Antibodies (Basel, Switzerland) · 2026Review
- Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026Article
- An efficient functional screening method for anti ATP hydrolase antibody based on fluorescent probes.Frontiers in immunology · 2026Article
- Emerging precision medicine in multiple myeloma: clinical and preclinical landscape of T cell, natural killer cell, and macrophages engaging multi-specific antibodies.Frontiers in immunology · 2026Review
- Antibody display technologies from phages to cells: translational bottlenecks and AI-enabled opportunities.Frontiers in bioengineering and biotechnology · 2026Review
- Structure-Guided Engineering of High-Affinity Antibodies Against Zika Virus Using Deep Learning and Molecular Dynamics.Chemistry & biodiversity · 2026Article
- Reprogramming the SARS-CoV-1 Neutralizing Antibody S230 to SARS-CoV-2 via Directed Evolution and Molecular Docking-Based Binding Mode Analysis.ACS synthetic biology · 2025Article
- AI-powered analysis of viral metagenomic sequencing data for rapid outbreak investigation and novel pathogen discovery.Frontiers in microbiology · 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
2 authors.
Funding
Abstract
The integration of high-throughput experimentation and machine learning is transforming data-driven antibody engineering, revolutionizing the discovery and optimization of antibody therapeutics. These approaches employ extensive datasets comprising antibody sequences, structures, and functional properties to train predictive models that enable rational design. This review highlights the significant advancements in data acquisition and feature extraction, emphasizing the necessity of capturing both sequence and structural information. We illustrate how machine learning models, including protein language models, are used not only to enhance affinity but also to optimize other crucial therapeutic properties, such as specificity, stability, viscosity, and manufacturability. Furthermore, we provide practical examples and case studies to demonstrate how the synergy between experimental and computational approaches accelerates antibody engineering. Finally, this review discusses the remaining challenges in fully realizing the potential of artificial intelligence (AI)-powered antibody discovery pipelines to expedite therapeutic development.
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.