Machine learning prediction of multiple distinct high-affinity chemotypes for α-synuclein fibrils.

Journal: Chemical communications (Cambridge, England)
Published Date: Feb 3, 2026

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.

Authors

  • Xinning Li
    Department of Orthopaedic Surgery, Boston University School of Medicine, Boston, MA, USA.
  • Ryann M Perez
    Department of Chemistry, School of Arts and Sciences, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA. [email protected].
  • Zhude Tu
    Department of Radiology, Washington University School of Medicine, St Louis, MO, 63110, USA.
  • Robert H Mach
    Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.
  • Sam Giannakoulias
  • E James Petersson

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