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Cyclic GMP-AMP synthase (cGAS) has emerged as a promising therapeutic target of several human diseases, including Alzheimer's disease (AD) and other neurodegenerative disorders. As a cytosolic DNA sensor, cGAS generates an innate immune response to promote neuroinflammation by producing an endogenous agonist of the stimulator of interferon genes (STING), 2'3'-cyclic GMP-AMP (cGAMP), which activates the cGAS-STING pathway. We have performed a high-throughput screening of a chemical library containing over 300K small molecules at the Fisher Drug Discovery Resource Center (DDRC), Rockefeller University (RU), to identify multiple hit inhibitors of human (h)-cGAS. We used a modified Kinase Glo® Luminescent Kinase assay, which was earlier developed at RU and later used by multiple groups, including ours, to perform primary screening of the library using h-cGAS. The hit candidates bearing novel scaffolds are structurally diverse and exhibited in vitro activity in the low micromolar range. or compound (cpd) , a sulfonamide derivative, is one of the most potent hits (IC =1.88 µM), selected for hit expansion and structure-activity relationship (SAR) analysis. We synthesized new analogs of and evaluated them in vitro against h-cGAS to identify (IC =0.66 µM) as the most potent hit analog. We further profiled and found that it modestly inhibited cGAMP levels by 29% at 30 µM in THP1 cells without detectable toxicity, and by 76% at 100 µM, albeit with a moderate decrease (∼20%) in cell viability. These results highlight a novel chemical series with promising in vitro activity, providing a starting point for the development of selective and potent human cGAS inhibitors for clinical use.
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http://dx.doi.org/10.1101/2025.08.18.670979 | DOI Listing |
Nanoscale
September 2025
School of Materials Science and Engineering, Beihang University, Beijing 100191, China.
The challenge of photocatalytic hydrogen production has motivated a targeted search for MXenes as a promising class of materials for this transformation because of their high mobility and high light absorption. High-throughput screening has been widely used to discover new materials, but the relatively high cost limits the chemical space for searching MXenes. We developed a deep-learning-enabled high-throughput screening approach that identified 14 stable candidates with suitable band alignment for water splitting from 23 857 MXenes.
View Article and Find Full Text PDFJ Chem Phys
September 2025
National Synchrotron Radiation Laboratory, State Key Laboratory of Advanced Glass Materials, Anhui Provincial Engineering Research Center for Advanced Functional Polymer Films, University of Science and Technology of China, Hefei, Anhui 230029, China.
Polymer density is a critical factor influencing material performance and industrial applications, and it can be tailored by modifying the chemical structure of repeating units. Traditional polymer density characterization methods rely heavily on domain expertise; however, the vast chemical space comprising over one million potential polymer structures makes conventional experimental screening inefficient and costly. In this study, we proposed a machine learning framework for polymer density prediction, rigorously evaluating four models: neural networks (NNs), random forest (RF), XGBoost, and graph convolutional neural networks (GCNNs).
View Article and Find Full Text PDFAngew Chem Int Ed Engl
September 2025
Department of Chemistry, University of Utah, Salt Lake City, UT, 84112, USA.
The term "generality" has recently been popularized in synthetic chemistry, owing largely to the increasing use of high-throughput technology for producing vast quantities of data and the emergence of data science tools to plan and interpret these experiments. Despite this, the term has not been clearly defined, and there is no standardized approach toward developing a method with a diverse (general) scope. This minireview will examine different emerging strategies toward achieving generality using selected examples and aims to give the reader an overview of modern workflows that have been used to expedite this pursuit.
View Article and Find Full Text PDFInt J Nanomedicine
September 2025
Department of Orthopedics, Honghui Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, People's Republic of China.
Peptide-based fluorescent probes have found widespread applications in biomedical research, including bio-imaging, disease diagnosis, drug discovery, and image-guided surgery. Their favorable properties-such as small molecular size, low toxicity, minimal immunogenicity, and high targeting specificity-have contributed to their growing utility in both basic research and translational medicine. This review provides a comprehensive overview of recent advances in peptide-based fluorescent probes, emphasizing design strategies, biological targets, and diverse functional applications.
View Article and Find Full Text PDFJ Phys Chem Lett
September 2025
Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.
In this work, we present a machine learning (ML) approach for predicting the optimal range separation parameters in transition metal complexes (TMCs), aiming to reduce the computational cost associated with optimally tuned range-separated hybrid (OT-RSH) functionals while preserving their accuracy. A data set containing 4380 TMCs was constructed by screening the tmQM database, with each TMC represented by a 62 087-dimensional multiple-fingerprint feature (MFF) vector and labeled with its optimally tuned range separation parameter. Multiple regression models were applied to train the prediction model, and the support vector machine (SVM) model yielded the best performance.
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