Identification of nanomolar adenosine A2A receptor ligands using reinforcement learning and structure-based drug design
- Morgan Thomas
- Pierre G. Matricon
- Robert J. Gillespie
- Maja Napiórkowska
- Hannah Neale
- Jonathan S. Mason
- Jason Brown
- Kaan Harwood
- Charlotte Fieldhouse
- Nigel A. Swain
- Tian Geng
- Noel M. O’Boyle
- Francesca Deflorian
- Andreas Bender
- Chris de Graaf
2025-07-01
Generative chemical language models (CLMs) have demonstrated success in learning language-based molecular representations for de novo drug design. Here, we integrate structure-based drug design (SBDD) principles with CLMs to go from protein structure to novel small-molecule ligands, without a priori knowledge of ligand chemistry. Using Augmented Hill-Climb, we successfully optimise multiple objectives within a practical timeframe, including protein-ligand complementarity. Resulting de novo molecules contain known or promising adenosine A 2A receptor ligand chemistry that is not available in commercial vendor libraries, accessing commercially novel areas of chemical space. Experimental validation demonstrates a binding hit rate of 88%, with 50% having confirmed functional activity, including three nanomolar ligands and two novel chemotypes. The two strongest binders are co-crystallised with the A 2A receptor, revealing their binding mechanisms that can be used to inform future iterations of structure-based de novo design, closing the AI SBDD loop.