The Gap Between Models and Tools I’ve seen a lot of impressive ML models in biopharma that never get used. Not because the science is wrong, but because the tool doesn’t fit into anyone’s workflow. The model might be published in Nature Methods with beautiful receiver operating characteristic curves, but if a discovery scientist can’t… Read More: Building ML Tools Scientists Will Actually Use »
The Features That Matter After training the developability model on 100+ therapeutic antibodies, I looked at the feature importance rankings. The model had learned to weight certain properties more heavily than others when making predictions. Some of these were obvious. Some were surprising. All of them tell us something about what actually makes antibodies difficult… Read More: What Makes an Antibody Hard to Manufacture? Data-Driven Insights »
Why Trastuzumab Is the Perfect Test Case When I built my antibody developability predictor, I knew I needed to validate it against a molecule where we actually know the manufacturing story. Trastuzumab (Herceptin) was the obvious choice. It’s one of the most successful therapeutic antibodies ever made, with decades of manufacturing data behind it. More… Read More: Case Study: Predicting Trastuzumab Developability »
The Inflection Point Three-four years ago, I stood in front of production equipment I’d designed using textbook equations and decades-old heuristics. The systems worked—they always had—but I couldn’t shake the feeling that we were leaving performance on the table. That nagging intuition led me down a rabbit hole of machine learning papers, Python tutorials, and… Read More: From Conventional Design to AI-Driven Innovation: A Strategic Journey in… »