To determine whether AI truly adds value, we need to ask if, and by how much, it changes a project’s outcome. That could mean synthesising fewer compounds, running fewer experiments, or finding even better compounds from your data.
Drawing on collaborations with global pharmaceutical teams, our CEO, Matt Segall, sets out what it takes to realise this in practice: a sound algorithm, real proof it works, deployment scientists can use, and a genuine shift in how teams work.
Through real-world examples, Matt shows how this approach has enabled teams to:
- Reach the same efficacious compounds while running a fraction of the experiments
- Recover active compounds wrongly discarded through missing, uncertain, or incorrect data
- Look beyond accuracy statistics like R² and RMSE to what genuinely improves a project’s outcome
- Understand when a prediction can be trusted, and when the uncertainty calls for more data
This presentation was given on 15 June, 2026 at Oxford Global Discovery & Development Europe.
Meet the speaker
Matt Segall, PhD
CEO, Optibrium
Matt has a Master of Science in Computation from the University of Oxford and a PhD in Theoretical Physics from the University of Cambridge.
As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualisation tools for drug discovery.
In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform.
Following the acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and, in 2009, led a management buyout of the StarDrop business to found Optibrium.
