ReviewBriefings in bioinformatics2024
Antibody design using deep learning: from sequence and structure design to affinity maturation.
Review in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- Trial Watch - bispecific T cell engagers and higher-order multispecific immunotherapeutics.Oncoimmunology · 2026Review
- ASD: antigen-specific antibody database.mAbs · 2026Article
- Mixture diffusion model for multimodal antibody design.Briefings in bioinformatics · 2026Article
- Adaptive Disorder as the Hallmark of Nanobodies Antigen-Binding Loops.Journal of chemical information and modeling · 2026Article
- Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics.Bioactive materials · 2026Review
- s_mmpbsa: A Lite and Cross-Platform MM-PBSA Program.Molecules (Basel, Switzerland) · 2026Article
- Context-aware multi-property antibody predictor: a novel framework integrating text and protein language models.NPJ systems biology and applications · 2026Article
- Repertoire-scale antibody structural prediction informs therapeutic design.Science advances · 2026Article
- Ab-SELDON: Leveraging Diversity Data for an Efficient Automated Computational Pipeline for Antibody Design.Journal of chemical information and modeling · 2026Article
- Ensemble molecular mimicry correlates with antibody cross-reactivity in proteome-wide studies.Frontiers in immunology · 2026Article
- Nanobodies in biomedicine: from molecular characteristics to fabrication and clinical translation.Military Medical Research · 2026Review
- Benchmarking antibody modeling tools across structure prediction, docking, and paratope-epitope interface analysis.Bioinformatics advances · 2026Article
- Fitness Landscape for Antibodies 2: Benchmarking Reveals That Protein AI Models Cannot Yet Consistently Predict Developability Properties.bioRxiv : the preprint server for biology · 2025Article
- Review
- Germline-aware deep learning models and benchmarks for predicting antibody VH-VL pairing.mAbs · 2025Article
- Leveraging artificial intelligence in antibody-drug conjugate development: from target identification to clinical translation in oncology.NPJ precision oncology · 2025Review
- ALLM-Ab: Active Learning-Driven Antibody Optimization Using Fine-Tuned Protein Language Models.Journal of chemical information and modeling · 2025Article
- Systematic evaluation of predictors for binding free energy changes upon mutations in protein complexes.Briefings in bioinformatics · 2025Article
- Enhancing antibody-antigen interaction prediction with atomic flexibility.PLoS computational biology · 2025Article
- High-throughput synthesis and specificity characterization of natively paired influenza hemagglutinin antibodies with oPoolScience translational medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Deep learning has achieved impressive results in various fields such as computer vision and natural language processing, making it a powerful tool in biology. Its applications now encompass cellular image classification, genomic studies and drug discovery. While drug development traditionally focused deep learning applications on small molecules, recent innovations have incorporated it in the discovery and development of biological molecules, particularly antibodies. Researchers have devised novel techniques to streamline antibody development, combining in vitro and in silico methods. In particular, computational power expedites lead candidate generation, scaling and potential antibody development against complex antigens. This survey highlights significant advancements in protein design and optimization, specifically focusing on antibodies. This includes various aspects such as design, folding, antibody-antigen interactions docking and affinity maturation.
Indexed as
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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.