ArticleJournal of medicinal chemistry2023
Molecular Graph-Based Deep Learning Algorithm Facilitates an Imaging-Based Strategy for Rapid Discovery of Small Molecules Modulating Biomolecular Condensates.
Article in Journal of medicinal chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- The emerging synergy of experimental and computational approaches for therapeutic modulation of biomolecular condensates.SLAS discovery : advancing life sciences R & D · 2026Review
- Deep Learning-Guided Discovery of Dual Inhibitors of SARS-CoV-2 Entry and 3CL Protease.Molecules (Basel, Switzerland) · 2026Article
- Discovery of TDP-43 aggregation inhibitorsbioRxiv : the preprint server for biology · 2026Article
- A Perspective on the Role of Mitochondrial Biomolecular Condensates (mtBCs) in Neurodegenerative Diseases and Evolutionary Links to Bacterial BCs.International journal of molecular sciences · 2025Review
- Liquid-Liquid Phase Separation Associated with Intrinsically Disordered Proteins: Experimental and Computational Tools.Current protein & peptide science · 2025Review
- Identification of Potent ADCK3 Inhibitors through Structure-Based Virtual Screening.Journal of chemical information and modeling · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Biomolecular condensates are proposed to cause diseases, such as cancer and neurodegeneration, by concentrating proteins at abnormal subcellular loci. Imaging-based compound screens have been used to identify small molecules that reverse or promote biomolecular condensates. However, limitations of conventional imaging-based methods restrict the screening scale. Here, we used a graph convolutional network (GCN)-based computational approach and identified small molecule candidates that reduce the nuclear liquid-liquid phase separation of TAR DNA-binding protein 43 (TDP-43), an essential protein that undergoes phase transition in neurodegenerative diseases. We demonstrated that the GCN-based deep learning algorithm is suitable for spatial information extraction from the molecular graph. Thus, this is a promising method to identify small molecule candidates with novel scaffolds. Furthermore, we validated that these candidates do not affect the normal splicing function of TDP-43. Taken together, a combination of an imaging-based screen and a GCN-based deep learning method dramatically improves the speed and accuracy of the compound screen for biomolecular condensates.
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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.