ArticleCommunications chemistry2023
Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning.
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- A p53Molecular therapy. Oncology · 2026Article
- Rapid Generation of High-Affinity Rabbit Anti-Mouse IgG Monoclonal Antibodies by High-Throughput Single-B-Cell Sorting and Recombinant Expression.Current issues in molecular biology · 2026Article
- Human monoclonal antibodies targeting α-Gal restrict IgE engagement of α-Gal syndrome allergens.The Journal of clinical investigation · 2026Article
- Artificial intelligence advancements in monoclonal antibody development technology.Frontiers in immunology · 2026Review
- Review
- Artificial intelligence in antibody design and development: harnessing the power of computational approaches.Medical & biological engineering & computing · 2025Review
- PLMFit: benchmarking transfer learning with protein language models for protein engineering.Briefings in bioinformatics · 2025Article
- Accelerating antibody discovery and optimization with high-throughput experimentation and machine learning.Journal of biomedical science · 2025Review
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Applying computational protein design to therapeutic antibody discovery - current state and perspectives.Frontiers in immunology · 2025Review
- High-affinity optimization potential of the virus neutralizing antibody with twin cysteine-stabilized complementarity-determining region 3.Frontiers in cellular and infection microbiology · 2025Article
- Monoclonal antibodies: From magic bullet to precision weapon.Molecular biomedicine · 2024Review
- Integrating Computational Design and Experimental Approaches for Next-Generation Biologics.Biomolecules · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.
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.