Machine learning prediction of multiple distinct high-affinity chemotypes for α-synuclein fibrils
Li X, Perez RM, Tu Z, Mach RH, Giannakoulias S, Petersson EJ. Machine learning prediction of multiple distinct high-affinity chemotypes for α-synuclein fibrils. Chem Commun (Camb). 2026 Jan 13. doi: 10.1039/d5cc06228d. Epub ahead of print. PMID: 41527506; PMCID: PMC12797025.
Abstract
To identify new ligands for positron emission tomography imaging of α-synuclein aggregates, we developed a machine learning model trained on <300 binding measurements. We used scaffold-guided curation to select a 30 compound prospective set from a 140-million-member library. Experimental validation yielded five high-affinity binders, showing robust generalization for ligand discovery.