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.
Show 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.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Benchmarking feature selection and feature extraction methods to improve the performances of machine-learning algorithms for patient classification using metabolomics biomedical data. 90%
- Machine learning driven acceleration of biopharmaceutical formulation development using Excipient Prediction Software (ExPreSo) 90%
- Topological embedding and directional feature importance in ensemble classifiers for multi-class classification 89%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.