9H2X
Crystal structure of stabilized A2A adenosine receptor A2AR-StaR2-bRIL in complex with compound 7, a novel nanomolar A2A receptor antagonist from modern hit-finding with structure-guided de novo design
This is a non-PDB format compatible entry.
Summary for 9H2X
| Entry DOI | 10.2210/pdb9h2x/pdb |
| Descriptor | Adenosine receptor A2a,Soluble cytochrome b562, SODIUM ION, CHOLESTEROL, ... (6 entities in total) |
| Functional Keywords | adenosine receptor, gpcr, antagonist, chemical hit, membrane protein |
| Biological source | Homo sapiens (human) More |
| Total number of polymer chains | 1 |
| Total formula weight | 55416.67 |
| Authors | |
| Primary citation | Thomas, M.,Matricon, P.G.,Gillespie, R.J.,Napiorkowska, M.,Neale, H.,Mason, J.S.,Brown, J.,Harwood, K.,Fieldhouse, C.,Swain, N.A.,Geng, T.,O'Boyle, N.M.,Deflorian, F.,Bender, A.,de Graaf, C. Identification of nanomolar adenosine A 2A receptor ligands using reinforcement learning and structure-based drug design. Nat Commun, 16:5485-5485, 2025 Cited by PubMed 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 A 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 receptor, revealing their binding mechanisms that can be used to inform future iterations of structure-based de novo design, closing the AI SBDD loop. PubMed: 40592852DOI: 10.1038/s41467-025-60629-0 PDB entries with the same primary citation |
| Experimental method | X-RAY DIFFRACTION (1.75 Å) |
Structure validation
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