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Related Experiment Video

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Multimodal framework for early developability assessment to accelerate protein and antibody development.

Jiayin Deng1, Qiong Huang2, Jiayi Lv3

  • 1The State Key Laboratory of Mechanism and Quality of Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Taipa, Macau 999078, China.

International Journal of Pharmaceutics
|February 22, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning framework, FormulationProtein, accurately predicts protein and antibody drug developability. This AI tool accelerates the formulation development process, reducing costly trial-and-error experiments for enhanced drug stability and safety.

Keywords:
Colloidal stabilityConformational stabilityDeep learningProtein/antibody formulationSolubilityViscosity

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Area of Science:

  • Biopharmaceutical development
  • Computational chemistry
  • Drug formulation

Background:

  • Protein and antibody therapeutics are vital in modern medicine but their clinical success relies on developability.
  • Formulation development is currently inefficient and expensive due to extensive trial-and-error experimentation.
  • Predicting developability requires understanding complex interactions between protein characteristics and formulation composition.

Purpose of the Study:

  • To develop a multimodal deep learning framework, FormulationProtein, for accurate prediction of protein formulation developability.
  • To create and utilize four large datasets covering conformational stability, colloidal stability, viscosity, and solubility.
  • To accelerate early-stage protein and antibody development through computational prediction.

Main Methods:

  • Construction of four developability-related datasets integrating protein and excipient information.
  • Development of a multimodal deep learning architecture (FormulationProtein) incorporating protein structure, sequence, and formulation data.
  • Application of transfer learning and conventional machine learning algorithms for feature representation and prediction.

Main Results:

  • FormulationProtein achieved high prediction accuracies: 0.925 for conformational stability, 0.858 for colloidal stability, 0.917 for viscosity, and 0.742 for solubility.
  • The framework successfully captured the complex interplay between protein attributes and formulation components.
  • Experimental validation confirmed the framework's predictive capabilities across multiple proteins and formulations.

Conclusions:

  • FormulationProtein offers a comprehensive computational approach for early-stage developability assessment.
  • This AI-driven framework significantly accelerates protein and antibody drug development.
  • The study demonstrates the potential of deep learning to optimize biopharmaceutical formulation.