ReviewMedical & biological engineering & computing2025
Artificial intelligence in antibody design and development: harnessing the power of computational approaches.
Review in Medical & biological engineering & computing, 2025. 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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry. Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry.
Indexed as
Identifiers
40887563What 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.