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bioRxiv · 10.1101/2024.11.11.622855

GRIP: physics-informed neural network for gradient retention time prediction in liquid chromatography

George, K.·Haeckl, F. P. J.·Grossmann, G.·Gurevich, A.·Tagirdzhanov, A.
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Abstract

Gradient liquid chromatography has numerous applications in life sciences. Retention time prediction remains challenging due to complex underlying physical processes and highly variable chromatographic conditions. Here, we present GRIP, a physics-informed neural network for gradient retention time prediction that explicitly uses experimental setup parameters. GRIP demonstrates zero-shot generalization to unseen chromatographic systems while being on par or out-performing the transfer learning-based baseline. This approach can computationally guide the experimental setup configuration tailored to specific compounds of interest.

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George, K., Haeckl, F. P. J., Grossmann, G., Gurevich, A., Tagirdzhanov, A.. 2024-11-12. GRIP: physics-informed neural network for gradient retention time prediction in liquid chromatography. https://doi.org/10.1101/2024.11.11.622855

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