January 21, 2026
Volume 26, Issue 2
Pages 689-1028
End-to-End System for Estimating Crystallization Kinetics Using a Deep Learning-Based Approach
Protein crystallization has emerging potential as an efficient and scalable purification approach in biopharmaceutical manufacturing. However, quantitative modeling of crystallization kinetics remains challenging due to limited experimental data quality, difficulty in detecting and characterizing crystals from noisy images, and lack of robustness in parameter estimation across diverse conditions. This study presents an end-to-end system for estimating crystallization kinetics by integrating deep learning-based image analysis with droplet-based crystallization experiments. Synthetic crystal images with realistic noise and morphological variations are employed to train robust models for crystal detection and dimension prediction. These predictions are then coupled with experimental time-series data to extract nucleation and growth data through single experiment-level preprocessing and batch-level optimization. The proposed system accurately identifies multiple crystals within individual droplets, predicts characteristic dimensions under noisy conditions, and constructs generalized kinetic models applicable across varying experimental conditions. This integrated and scalable approach provides a foundation for automated, real-time crystallization monitoring and offers a pathway to real-time kinetic modeling and process optimization in biopharmaceutical applications.
- Han Bit Kim
- Young Hyun Cho
- Moo Sun Hong
https://pubs.acs.org/doi/10.1021/acs.cgd.5c01429
January 21, 2026
Volume 26, Issue 2
Pages 689-1028
End-to-End System for Estimating Crystallization Kinetics Using a Deep Learning-Based Approach
Protein crystallization has emerging potential as an efficient and scalable purification approach in biopharmaceutical manufacturing. However, quantitative modeling of crystallization kinetics remains challenging due to limited experimental data quality, difficulty in detecting and characterizing crystals from noisy images, and lack of robustness in parameter estimation across diverse conditions. This study presents an end-to-end system for estimating crystallization kinetics by integrating deep learning-based image analysis with droplet-based crystallization experiments. Synthetic crystal images with realistic noise and morphological variations are employed to train robust models for crystal detection and dimension prediction. These predictions are then coupled with experimental time-series data to extract nucleation and growth data through single experiment-level preprocessing and batch-level optimization. The proposed system accurately identifies multiple crystals within individual droplets, predicts characteristic dimensions under noisy conditions, and constructs generalized kinetic models applicable across varying experimental conditions. This integrated and scalable approach provides a foundation for automated, real-time crystallization monitoring and offers a pathway to real-time kinetic modeling and process optimization in biopharmaceutical applications.
https://pubs.acs.org/doi/10.1021/acs.cgd.5c01429