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Gerges, Amgad and Canning, Una (2026) AI-assisted prioritisation of novel SSTR2 ligands for high-risk stage 4 neuroblastoma: integrating generative analogue design, deep-learning ADMET prediction and retrosynthetic planning. AI Chemistry, 1(4) (16). pp. 1-34. ISSN 3042-6723
Somatostatin receptor subtype 2 (SSTR2), encoded at chromosome 17q25.1, is overexpressed in high-risk stage 4 neuroblastoma with a 17q gain, yet no non-peptide small-molecule ligand exists for this indication. From the agonist L-054,522, BROOD generated 5766 analogues, of which 292 were docked to SSTR2 (PDB 7XN9) with FRED, HYBRID, and POSIT; no physicochemical filter was applied. The query achieved an ROC AUC of 0.798 against known ligands and decoys. Eight compounds reaching GREAT pose confidence were re-docked with AutoDock Vina Extended, FITTED, Flare, and DiffDock and then assessed across five ADMET and two retrosynthesis platforms. Redocking the crystallographic ligand recovered the experimental pose with four site-directed programs (RMSD 0.85–1.79 Å), but none run blind or as ensembles, indicating that pose recovery depends on the search protocol rather than the scoring function. Docking scores were not reproducible across repeat runs, so candidates are not ranked. Compound 8 reproduced the Asp122, Gln126, and Thr194 contacts, alone received a Spaya RScore of 1.0 indicating a fully precedented route, and it held its pose over three 100 ns molecular dynamic simulations in a POPC bilayer (ligand RMSD 1.70 ± 0.12 Å). Because docking cannot distinguish agonism from antagonism, this work delivers a synthesisable hypothesis set, not a characterised pharmacological agent.
Keywords: artificial intelligence; machine learning; deep learning; virtual screening; retrosynthesis prediction; ADMET prediction; somatostatin receptor 2; neuroblastoma; molecular docking; structure–property relationship
Available under License Creative Commons Attribution 4.0.
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