ArticleNature chemical biology2024
Discovery of potent inhibitors of α-synuclein aggregation using structure-based iterative learning.
Article in Nature chemical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 39 citations in OpenAlex.
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- Peripheral Neuropathy in Metabolic Stress.International journal of molecular sciences · 2026Review
- From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Cell-based regenerative interventions in Parkinson's disease: Overcoming pharmacological limitations.Acta pharmaceutica Sinica. B · 2026Review
- Molecular Interplay of Small Molecules and Calcium Ions with α-Synuclein Revealed by NMR and Molecular Dynamics Simulations.ACS chemical neuroscience · 2026Article
- Rapid elongation drives the exceptionally fast aggregation of the most common localized human amyloid medin.Communications chemistry · 2026Article
- Review
- Foldamers rescue synucleinopathy phenotypes in multiple in vitro and in vivo models.Science translational medicine · 2026Article
- The Use of Artificial Intelligence in Improving Diagnostic Modalities in Rheumatoid Arthritis: A Narrative Review.Cureus · 2026Review
- Artificial Intelligence for Natural Products Drug Discovery in Neurodegenerative Therapies: A Review.Biomolecules · 2026Review
- Identification of potent high-affinity secondary nucleation inhibitors of Aβ42 aggregation from an ultra-large chemical library using deep docking.Molecular systems biology · 2026Article
- Prions and protein aggregates as pathogens, self-propagating structures, biomarkers, and therapeutic targets.Microbiology and molecular biology reviews : MMBR · 2025Review
- Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay.Journal of the American Chemical Society · 2025Article
- Design of Ig-like binders targeting α-synuclein fibril for mitigating its pathological activities.Nature communications · 2025Article
- Detecting alpha-synuclein aggregates with small molecules on single-molecule array.Chemical science · 2025Article
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- A mechanistic model of pure and lipidic α-synuclein aggregation for advancing Parkinson's therapies.Communications chemistry · 2025Article
- Substrate-based discovery of α-hydroxycarboxylic acid derivatives as potential herbicides targeting dihydroxyacid dehydratase.Nature communications · 2025Article
- Batched Bayesian Optimization by Maximizing the Probability of Including the Optimum.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 3 countries.
Funding
Abstract
Machine learning methods hold the promise to reduce the costs and the failure rates of conventional drug discovery pipelines. This issue is especially pressing for neurodegenerative diseases, where the development of disease-modifying drugs has been particularly challenging. To address this problem, we describe here a machine learning approach to identify small molecule inhibitors of α-synuclein aggregation, a process implicated in Parkinson's disease and other synucleinopathies. Because the proliferation of α-synuclein aggregates takes place through autocatalytic secondary nucleation, we aim to identify compounds that bind the catalytic sites on the surface of the aggregates. To achieve this goal, we use structure-based machine learning in an iterative manner to first identify and then progressively optimize secondary nucleation inhibitors. Our results demonstrate that this approach leads to the facile identification of compounds two orders of magnitude more potent than previously reported ones.
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Registered trials
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.