Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models

Pedro Seber, Richard Braatz
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:2611-2619, 2025.

Abstract

N-glycosylation has many essential biological roles, and is important for biotherapeutics as it can affect drug efficacy, duration of effect, and toxicity. The prediction of N-glycosylation and other important biopharmaceutical production values have mostly been limited to mechanistic modeling. We present a residual hybrid modeling approach that integrates mechanistic modeling with machine learning to produce significantly more accurate predictions for N-glycosylation and bioproduction. For the largest dataset, the residual hybrid models have an average 736-fold reduction in testing prediction error. Furthermore, the residual hybrid models have lower prediction errors than the mechanistic models for all of the predicted variables in the datasets. We provide the automatic machine learning software used in this work, allowing reproduction and use of our software for other tasks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v258-seber25a, title = {Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models}, author = {Seber, Pedro and Braatz, Richard}, booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics}, pages = {2611--2619}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/seber25a/seber25a.pdf}, url = {https://proceedings.mlr.press/v258/seber25a.html}, abstract = {N-glycosylation has many essential biological roles, and is important for biotherapeutics as it can affect drug efficacy, duration of effect, and toxicity. The prediction of N-glycosylation and other important biopharmaceutical production values have mostly been limited to mechanistic modeling. We present a residual hybrid modeling approach that integrates mechanistic modeling with machine learning to produce significantly more accurate predictions for N-glycosylation and bioproduction. For the largest dataset, the residual hybrid models have an average 736-fold reduction in testing prediction error. Furthermore, the residual hybrid models have lower prediction errors than the mechanistic models for all of the predicted variables in the datasets. We provide the automatic machine learning software used in this work, allowing reproduction and use of our software for other tasks.} }
Endnote
%0 Conference Paper %T Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models %A Pedro Seber %A Richard Braatz %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-seber25a %I PMLR %P 2611--2619 %U https://proceedings.mlr.press/v258/seber25a.html %V 258 %X N-glycosylation has many essential biological roles, and is important for biotherapeutics as it can affect drug efficacy, duration of effect, and toxicity. The prediction of N-glycosylation and other important biopharmaceutical production values have mostly been limited to mechanistic modeling. We present a residual hybrid modeling approach that integrates mechanistic modeling with machine learning to produce significantly more accurate predictions for N-glycosylation and bioproduction. For the largest dataset, the residual hybrid models have an average 736-fold reduction in testing prediction error. Furthermore, the residual hybrid models have lower prediction errors than the mechanistic models for all of the predicted variables in the datasets. We provide the automatic machine learning software used in this work, allowing reproduction and use of our software for other tasks.
APA
Seber, P. & Braatz, R.. (2025). Improving N-Glycosylation and Biopharmaceutical Production Predictions Using AutoML-Built Residual Hybrid Models. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:2611-2619 Available from https://proceedings.mlr.press/v258/seber25a.html.

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