ReviewmAbs
Discovery-stage identification of drug-like antibodies using emerging experimental and computational methods.
Review in mAbs. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Article
- Contrastive learning enables epitope overlap predictions for targeted antibody discovery.Patterns (New York, N.Y.) · 2026Article
- Ultra-Dilute Developability Analysis of Antibody Self-Association and Non-Specific Binding.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- Facile generation of drug-like conformational antibodies specific for amyloid fibrils.Nature chemical biology · 2025Article
- Prediction of Self-Association and Solution Behavior of Monoclonal Antibodies Using the QCM-D Metric of Loosely Interacting Layer.Molecular pharmaceutics · 2025Article
- Human antibody polyreactivity is governed primarily by the heavy-chain complementarity-determining regions.Cell reports · 2024Article
- The medicinal chemistry evolution of antibody-drug conjugates.RSC medicinal chemistry · 2024Review
- Editorial: Progress and challenges in computational structure-based design and development of biologic drugs.Frontiers in molecular biosciences · 2024Article
- Prospects for the computational humanization of antibodies and nanobodies.Frontiers in immunology · 2024Review
- Recent advances in anti-inflammatory active components and action mechanisms of natural medicines.Inflammopharmacology · 2023Review
- Simplifying complex antibody engineering using machine learning.Cell systems · 2023Review
- CRISPR/Cas9 Genome Editing for Tissue-Specific In Vivo Targeting: Nanomaterials and Translational Perspective.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Physiologically Based Pharmacokinetic Modeling to Characterize the Effect of Molecular Charge on Whole-Body Disposition of Monoclonal Antibodies.The AAPS journal · 2023Article
- Analytical Workflows to Unlock Predictive Power in Biotherapeutic Developability.Pharmaceutical research · 2023Article
- AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification ofFrontiers in molecular biosciences · 2023Article
- Article
- Joined at the hip: The role of light chain complementarity determining region 2 in antibody self-association.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Co-optimization of therapeutic antibody affinity and specificity using machine learning models that generalize to novel mutational space.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
There is intense and widespread interest in developing monoclonal antibodies as therapeutic agents to treat diverse human disorders. During early-stage antibody discovery, hundreds to thousands of lead candidates are identified, and those that lack optimal physical and chemical properties must be deselected as early as possible to avoid problems later in drug development. It is particularly challenging to characterize such properties for large numbers of candidates with the low antibody quantities, concentrations, and purities that are available at the discovery stage, and to predict concentrated antibody properties (e.g., solubility, viscosity) required for efficient formulation, delivery, and efficacy. Here we review key recent advances in developing and implementing high-throughput methods for identifying antibodies with desirable
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.