
AI/ML Now: Bayesian Optimization and Automation for siRNA Method Development (Part Two)
Ed Ahearne highlights the benefits of ML-driven chromatography for the development of biopharmaceutical products

‘Edward Ahearne is a Senior Analytical Scientist in AstraZeneca's Early Chemical Development New Modalities group. He develops data-driven approaches to chromatographic method development across small molecules, oligonucleotides, peptides, and conjugates. Edward contributed to the development of BAMO (Bayesian Automated Method Optimiser), a closed-loop, fully autonomous platform that applies Bayesian optimisation to liquid chromatography. He applied BAMO to the separation of siRNA diastereomers, where it matched the results of manual method development while reducing development time from approximately three weeks to two days. Before joining AstraZeneca, Edward worked at Pfizer and GW Pharmaceuticals. He holds an MSc in which he applied graph neural networks to NMR chemical shift prediction. He is also the founder and Chief Data Officer of Phantom Therapeutics, an AI biotech start-up focused on cancer of unknown primary.

Ed Ahearne highlights the benefits of ML-driven chromatography for the development of biopharmaceutical products

Ed Ahearne combined automation, machine learning, and Bayesian optimization to speed up method development for siRNA medicines.