DOTAD: A Database of Therapeutic Antibody Developability

No abstract is available for this paper.

• Publication year

  2024

• Venue

  Interdisciplinary Sciences Computational Life Sciences

• Publication date

  2024-03-26

• Fields of study

  Medicine, Computer Science

• Identifiers
  DOI 10.1007/s12539-024-00613-2PMID 38530613
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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• Predictive modeling of concentration-dependent viscosity behavior of monoclonal antibody solutions using artificial neural networks

  2023cited by this paper
• Deep learning in preclinical antibody drug discovery and development.

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• LambdaPP: Fast and accessible protein‐specific phenotype predictions

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• iProtGly-SS: A Tool to Accurately Predict Protein Glycation Site Using Structural-Based Features.

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• Development of QSAR models for in silico screening of antibody solubility

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• DeepSCM: An efficient convolutional neural network surrogate model for the screening of therapeutic antibody viscosity

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• The global landscape of approved antibody therapies

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• Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

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• Machine Learning Applied to Determine the Molecular Descriptors Responsible for the Viscosity Behavior of Concentrated Therapeutic Antibodies.

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• Interaction with 2,4-dinitrophenol correlates with polyreactivity, self-binding, and stability of clinical-stage therapeutic antibodies.

  2021cited by this paper
• Intrinsic physicochemical profile of marketed antibody-based biotherapeutics

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• BioPhi: A platform for antibody design, humanization, and humanness evaluation based on natural antibody repertoires and deep learning

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• Humanization of antibodies using a machine learning approach on large-scale repertoire data

  2021cited by this paper
• SVMTriP: A Method to Predict B-Cell Linear Antigenic Epitopes.

  2020cited by this paper
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  2020cited by this paper
• Interaction of clinical-stage antibodies with heme predicts their physiochemical and binding qualities

  2020cited by this paper
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  2020cited by this paper
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  2020cited by this paper
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  2020cited by this paper
• PATH - Prediction of Amyloidogenicity by Threading and Machine Learning

  2020cited by this paper
• Insight into the protein solubility driving forces with neural attention

  2020cited by this paper
• [Biosimilars: opportunities and risks].

  2020cited by this paper
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  2020cited by this paper
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  2020cited by this paper
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  2020cited by this paper
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  2019cited by this paper
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  2019cited by this paper
• Charge and hydrophobicity are key features in sequence-trained machine learning models for predicting the biophysical properties of clinical-stage antibodies

  2019cited by this paper
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  2019cited by this paper
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  2019cited by this paper
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  2019cited by this paper
• Heparin chromatography as an in vitro predictor for antibody clearance rate through pinocytosis

  2019cited by this paper
• Advancing therapeutic protein discovery and development through comprehensive computational and biophysical characterization.

  2019cited by this paper
• RFAmyloid: A Web Server for Predicting Amyloid Proteins

  2018cited by this paper
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  2018cited by this paper
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  2018cited by this paper
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  2018cited by this paper
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  2018cited by this paper
• DeepSol: a deep learning framework for sequence‐based protein solubility prediction

  2018cited by this paper
• Locking the Elbow: Improved Antibody Fab Fragments as Chaperones for Structure Determination.

  2017cited by this paper
• Next generation antibody drugs: pursuit of the 'high-hanging fruit'

  2017cited by this paper
• In-silico prediction of concentration-dependent viscosity curves for monoclonal antibody solutions

  2017cited by this paper
• Prediction of delayed retention of antibodies in hydrophobic interaction chromatography from sequence using machine learning

  2017cited by this paper
• Protein–Sol: a web tool for predicting protein solubility from sequence

  2017cited by this paper
• The state‐of‐play and future of antibody therapeutics☆

  2017cited by this paper
• BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes

  2017cited by this paper
• Amyloidogenic motifs revealed by n-gram analysis

  2017cited by this paper
• Tocilizumab: A Review in Rheumatoid Arthritis

  2017cited by this paper
• Hydrophobic Interaction Chromatography.

  2017cited by this paper
• Biophysical properties of the clinical-stage antibody landscape

  2017influential reference
• iCar-PseCp: identify carbonylation sites in proteins by Monte Carlo sampling and incorporating sequence coupled effects into general PseAAC

  2016cited by this paper
• Engineering the surface properties of a human monoclonal antibody prevents self-association and rapid clearance in vivo

  2016cited by this paper
• An alternative assay to hydrophobic interaction chromatography for high-throughput characterization of monoclonal antibodies

  2015cited by this paper
• A novel screening method to assess developability of antibody-like molecules

  2015cited by this paper
• Prediction of Peptide and Protein Propensity for Amyloid Formation

  2014cited by this paper
• GAP: towards almost 100 percent prediction for β-strand-mediated aggregating peptides with distinct morphologies

  2014cited by this paper
• CarSPred: A Computational Tool for Predicting Carbonylation Sites of Human Proteins

  2014cited by this paper
• High-throughput screening for developability during early-stage antibody discovery using self-interaction nanoparticle spectroscopy

  2014cited by this paper
• Structure-Based Prediction of Asparagine and Aspartate Degradation Sites in Antibody Variable Regions

  2014cited by this paper
• Early developability screen of therapeutic antibody candidates using Taylor dispersion analysis and UV area imaging detection

  2014cited by this paper
• In silico selection of therapeutic antibodies for development: Viscosity, clearance, and chemical stability

  2014cited by this paper
• MetAmyl: A METa-Predictor for AMYLoid Proteins

  2013cited by this paper
• Addressing polyspecificity of antibodies selected from an in vitro yeast presentation system: a FACS-based, high-throughput selection and analytical tool.

  2013cited by this paper
• High throughput detection of antibody self-interaction by bio-layer interferometry

  2013cited by this paper
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  2013cited by this paper
• In silico Platform for Prediction of N-, O- and C-Glycosites in Eukaryotic Protein Sequences

  2013cited by this paper
• Monoclonal antibody therapeutics: history and future.

  2012cited by this paper
• Molecular engineering of antibodies for therapeutic and diagnostic purposes

  2012cited by this paper
• A strategy for risk mitigation of antibodies with fast clearance

  2012cited by this paper
• PROSO II – a new method for protein solubility prediction

  2012cited by this paper
• PredSulSite: prediction of protein tyrosine sulfation sites with multiple features and analysis.

  2012cited by this paper
• High-throughput analysis of concentration-dependent antibody self-association.

  2011cited by this paper
• High-throughput prediction of protein antigenicity using protein microarray data

  2010cited by this paper
• Polyreactivity increases the apparent affinity of anti-HIV antibodies by heteroligation

  2010cited by this paper
• Musite, a Tool for Global Prediction of General and Kinase-specific Phosphorylation Sites*

  2010cited by this paper
• Structure and function of immunoglobulins.

  2010cited by this paper
• Identification of conformational B-cell Epitopes in an antigen from its primary sequence

  2010cited by this paper
• SOLpro: accurate sequence-based prediction of protein solubility

  2009cited by this paper
• COBEpro: a novel system for predicting continuous B-cell epitopes.

  2009cited by this paper
• Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

  2009cited by this paper
• NetCSSP: web application for predicting chameleon sequences and amyloid fibril formation

  2009cited by this paper
• Cross-Interaction Chromatography: A Rapid Method to Identify Highly Soluble Monoclonal Antibody Candidates

  2009cited by this paper
• Predicting linear B‐cell epitopes using string kernels

  2008cited by this paper
• Prediction of continuous B‐cell epitopes in an antigen using recurrent neural network

  2006cited by this paper

• Ginkgo Datapoints Antibody Developability Competition outcomes: limited model performance and a call for data standardization.

  2026cites this paper
• Trends and challenges in bispecific antibody production.

  2025cites this paper
• A comprehensive antigen-antibody complex database unlocking insights into interaction interface

  2025cites this paper
• Artificial intelligence in therapeutic antibody design: Advances and future prospects.

  2025cites this paper
• OmniNeo: a multi-omics pipeline incorporating proteomics and AI selection for neoantigen optimization in tumor immunotherapy

  2025cites this paper
• Enhancing polyreactivity prediction of preclinical antibodies through fine-tuned protein language models

  2025cites this paper
• Drug-like antibody design against challenging targets with atomic precision

  2025cites this paper
• Ab-amy 2.0: Predicting light chain amyloidogenic risk of therapeutic antibodies based on antibody language model.

  2024cites this paper
• AbNumPro: A comprehensive offline toolkit for antibody numbering and antigen-binding region prediction

  2024cites this paper