ArticleNature biomedical engineering2024
Optimization of therapeutic antibodies for reduced self-association and non-specific binding via interpretable machine learning.
Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 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
32 citing papers in PubMed, 39 citations in OpenAlex.
- Article
- Article
- Article
- Efficient inference of non-polyreactive antibody variants dependent on local fine-tuning.mAbs · 2026Article
- Predicting non-specific binding of VHHs using machine learning models with cluster-aware validation.mAbs · 2026Article
- Article
- Beyond affinity: AI-supported developability assessment and multi-objective optimization in antibody development.Antibody therapeutics · 2026Review
- A Synthetic Platform for Antibody Junctional Diversification Beyond Natural Constraints.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-Driven Design Platforms of Next-Generation Antibody Therapeutics.Topics in current chemistry (Cham) · 2026Review
- Impact of heme on the therapeutic efficacy of anti-CD20 antibodies.The Journal of biological chemistry · 2026Article
- An intrinsic sequence-structural profile for mRNA-delivered therapeutic antibodies.Briefings in bioinformatics · 2026Article
- Characterising nanobody developability to improve therapeutic design using the Therapeutic Nanobody Profiler.Communications biology · 2026Article
- AI-assisted protein design to rapidly convert antibody sequences to intrabodies targeting diverse peptides and histone modifications.Science advances · 2026Article
- Ultra-Dilute Developability Analysis of Antibody Self-Association and Non-Specific Binding.Methods in molecular biology (Clifton, N.J.) · 2026Article
- PROPERMAB: an integrative framework formAbs · 2025Article
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- Article
- ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models.Journal of chemical information and modeling · 2025Article
- Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.Journal of biomedical science · 2025Review
- Antibody-Nanoparticle Conjugates in Therapy: Combining the Best of Two Worlds.Small (Weinheim an der Bergstrasse, Germany) · 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
10 authors at 3 institutions in 1 country.
Funding
Abstract
Antibody development, delivery, and efficacy are influenced by antibody-antigen affinity interactions, off-target interactions that reduce antibody bioavailability and pharmacokinetics, and repulsive self-interactions that increase the stability of concentrated antibody formulations and reduce their corresponding viscosity. Yet identifying antibody variants with optimal combinations of these three types of interactions is challenging. Here we show that interpretable machine-learning classifiers, leveraging antibody structural features descriptive of their variable regions and trained on experimental data for a panel of 80 clinical-stage monoclonal antibodies, can identify antibodies with optimal combinations of low off-target binding in a common physiological-solution condition and low self-association in a common antibody-formulation condition. For three clinical-stage antibodies with suboptimal combinations of off-target binding and self-association, the classifiers predicted variable-region mutations that optimized non-affinity interactions while maintaining high-affinity antibody-antigen interactions. Interpretable machine-learning models may facilitate the optimization of antibody candidates for therapeutic applications.
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.