Chromatography is a key unit operation in the biopharmaceutical manufacturing process used for protein purification and polishing. Design and optimization of these processes are resource-intensive resulting from the complex combinatorial design space. This constraint combined with the wide diversity in therapeutic formats and increased pressure for timely delivery to the market necessitates an efficient, fast, robust and generalized framework for process design and optimization. Here we present Gaussian processes as a potent machine learning methodology for predictive modeling in the context of QSAR modeling used for resin and solvent condition selection. We highlight the on-par predictive power of Gaussian Processes with other reported machine learning algorithms. Furthermore, we demonstrate additional properties of Gaussian processes such as its ability to provide confidence estimates for its prediction that makes it suitable for model-assisted optimization. Finally, we demonstrate the possibility to derive feature importances from Gaussian processes, making these models as interpretable as ensembled tree-based methods such as random forests.
Narayanan et al. (Sat,) studied this question.