SOURCE-LINKED INTELLIGENCE
Cracking the Radiochemical Black Box with Bayesian Optimisation
s has been hindered not by a lack of promising tracer molecules, but by the difficulty of developing efficient radiochemical reactions to reliably produce them. This research will develop open-source machine learning tools to guide and accelerate these types of reactions. These tools will be validated using real radiochemical datasets, demonstrated on the synthesis of clinically relevant [¹⁸F]-radiotracers, and applied in the discovery of new radiochemical reactions. By doing so, this research will support a broad community of chemists, medical imaging specialists, and pharmaceutical scientists in harnessing the full potential of PET for studying and diagnosing disease. Radiolabeling, Bayesian Optimisation, Machine Learning
Read original source ↗ Open in workspace
- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 260347.92
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T05:31:32.981Z. This is not the publication date.