ArticleNature reviews bioengineering2026
Multi-objective antibody design and optimization using machine learning.
Article in Nature reviews bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Antibodies, ranging from conventional immunoglobulins to single-domain and bispecific antibodies, are developed for a wide spectrum of therapeutic and diagnostic applications. However, lead antibodies are commonly engineered to optimize one or more key properties, including affinity, specificity, polyreactivity, stability, aggregation, and immunogenicity, and tradeoffs among these attributes frequently hinder the development of variants suitable for clinical use. Furthermore, identifying lead candidates with desirable properties typically requires multiple rounds of experimental screening and evaluation, particularly when targeting pre-selected epitopes or challenging, high-value targets such as G protein-coupled receptors (GPCRs), ion channels, and multipass membrane proteins. Artificial intelligence (AI) and machine learning (ML) methods can streamline experimental screening and antibody generation methods. In this Review, we discuss both computational multi-objective optimization of existing antibodies and de novo antibody design. The application of AI/ML methods has the potential to overcome current bottlenecks in antibody development and accelerate the generation of improved therapeutics for human health.
Identifiers
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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.