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Related Experiment Video

Updated: Mar 6, 2026

Author Spotlight: Advancing Biotherapeutic Mass Calculation by Introducing mAbScale, a Python-Based Desktop Application
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Molecular Modeling and machine learning for predicting high-concentration antibody viscosity.

Dariya Baizhigitova1, I-En Wu1, Lateefat Kalejaye1

  • 1Department of Chemical Engineering and Materials Science, Stevens Institute of Technology, Hoboken, NJ 07030, United States.

Advanced Drug Delivery Reviews
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models can now predict antibody viscosity, accelerating the development of high-concentration antibody formulations for subcutaneous delivery. This approach reduces the need for time-intensive experiments, optimizing drug development.

Keywords:
Antibody viscosityHigh-concentration formulationMachine learningMolecular modeling

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Area of Science:

  • Biopharmaceutical Development
  • Computational Chemistry
  • Machine Learning Applications

Background:

  • High-concentration monoclonal antibody formulations are crucial for subcutaneous delivery but face challenges due to high viscosity.
  • Accurate viscosity prediction is vital for formulation optimization but experimental methods are resource- and time-intensive.
  • In-silico methods, including machine learning (ML) and molecular modeling, offer promising solutions for early-stage viscosity screening.

Purpose of the Study:

  • To provide a comprehensive review of recent advances in ML-based approaches for predicting high-concentration antibody viscosity.
  • To highlight the integration of ML and molecular modeling into antibody developability pipelines.
  • To offer a roadmap for researchers utilizing these computational methods for accelerated formulation development.

Main Methods:

  • Review of ML-based techniques for antibody viscosity prediction.
  • Discussion of key aspects including dataset generation, feature engineering, model training, validation, and interpretation.
  • Exploration of model deployment strategies in biopharmaceutical research.

Main Results:

  • Significant advancements in ML and molecular modeling enable accurate early-stage antibody viscosity screening.
  • These in-silico tools can reduce the reliance on extensive experimental assessments.
  • The review synthesizes current capabilities and identifies areas for future development in ML-driven viscosity prediction.

Conclusions:

  • ML and molecular modeling are powerful tools for accelerating the development of high-concentration antibody formulations.
  • Integrating these computational methods can streamline the formulation optimization process.
  • Further research and development in this area will drive innovation in biopharmaceutical drug delivery.