Evidence map›Paper›PMID 41527506›Full record

ArticleChemical communications (Cambridge, England)2026

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

Xinning Li, Ryann M Perez, Zhude Tu, Robert H Mach, Sam Giannakoulias, E James Petersson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Chemical communications (Cambridge, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xinning LiDepartment of Chemistry, School of Arts and Sciences, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA. ejpetersson@sas.upenn.edu.ORCID http://orcid.org/0009-0008-6279-5810
Ryann M PerezDepartment of Chemistry, School of Arts and Sciences, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA. ejpetersson@sas.upenn.edu.ORCID http://orcid.org/0000-0003-2233-8910
Zhude TuDepartment of Radiology, Washington University School of Medicine, St Louis, MO, 63110, USA.ORCID http://orcid.org/0000-0003-0325-835X
Robert H MachDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, USA.ORCID http://orcid.org/0000-0002-7645-2869
Sam GiannakouliasDivision for Advanced Computation, Sentauri Inc., Glenwood, MD 21738, USA.ORCID http://orcid.org/0000-0003-1830-6369
E James PeterssonDepartment of Chemistry, School of Arts and Sciences, University of Pennsylvania, 231 South 34th Street, Philadelphia, PA 19104, USA. ejpetersson@sas.upenn.edu.ORCID http://orcid.org/0000-0003-3854-9210

Funding

Medicinal Chemistry CoreU19NS110456 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI ROBERT H MACH · 2019 to 2026
$44.1M
Predoctoral Training at the Chemistry-Biology InterfaceT32GM133398 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI Ronen Marmorstein, Ernest James Petersson · 2020 to 2026
$2.4M
Combining Chemical Biology and Machine Learning to Generate Reproducible Amyloid FibrilsF31AG090063 · NIA · UNIVERSITY OF PENNSYLVANIA · PI PEREZ, RYANN MICHAEL · 2024 to 2024
$45k
NIA NIH HHS F31 AG090063NIGMS NIH HHS T32 GM133398NINDS NIH HHS U19 NS110456
6 · The paper itself

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.

Indexed as

alpha-SynucleinMachine LearningHumansLigandsPositron-Emission TomographyPredictive Learning ModelsProtein Bindingalpha-SynucleinLigands

Identifiers

PMID41527506
PMCPMC12797025

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.