Rethinking Chromatography Workflows with AI and Machine Learning

April 1, 2025

Before using AI/ML to analyze chromatographic data (or any other mode of data), you must provide both high-quality training data and well-annotated relevant metadata. This, in turn, hinges on having the appropriate workflows in place for the collection and management of chromatography data. We recently spoke with developability and protein production groups at major biopharma organizations across the industry about their data workflows and discovered several shared challenges that impact not only the ability to use AI/ML, but overall operational efficiency and effectiveness.

This article explores the key hurdles that must be overcome before we can use AI/ML with separation science data. We then offer an approach to managing these data that can overcome these challenges.