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 () and Chris Graaf ()
Additional contact information
Morgan Thomas: University of Cambridge
Pierre G. Matricon: Great Abington
Robert J. Gillespie: Great Abington
Maja Napiórkowska: Great Abington
Hannah Neale: Great Abington
Jonathan S. Mason: Great Abington
Jason Brown: Great Abington
Kaan Harwood: Great Abington
Charlotte Fieldhouse: Great Abington
Nigel A. Swain: Great Abington
Tian Geng: Great Abington
Noel M. O’Boyle: Great Abington
Francesca Deflorian: Great Abington
Andreas Bender: University of Cambridge
Chris Graaf: Great Abington

Nature Communications, 2025, vol. 16, issue 1, 1-14

Abstract: Abstract 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 A2A 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 A2A receptor, revealing their binding mechanisms that can be used to inform future iterations of structure-based de novo design, closing the AI SBDD loop.

Date: 2025
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DOI: 10.1038/s41467-025-60629-0

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