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Single-atom nanozymes (SAzymes), with their superior enzyme-like catalytic activity, have emerged as promising candidates for oncology therapeutics. The well-defined structures of SAzymes make them well predictable by experiences and theoretical calculation. However, the effects of metal center species and coordination environments on enzyme-like activity are variable, and screening catalytic activity by artificial experiments is challenging. High-throughput screening can rapidly select the activity center structures of SAzymes with optimal enzyme-like activity, thus their better application in tumor therapy is highly desirable. Herein, a "high-throughput screening-SAzymes structures" system is established for efficient oncology drug preparation by density functional theory for oxidase-like processes and screened the differences brought about by different metals and coordination environments. Through this screening process, SAzymes with transition metals (Mn, Fe, Co, Ni) as active centers are synthesized and then tested the multi-enzyme activities. It is found that the SAzyme with Co as the active metal center exhibited the best oxidase-like activity, and the system further showed good anti-oral squamous cell carcinoma properties both in vitro and in vivo. This study opens up a new avenue for the rational design of SAzymes in oral cancer therapy by combining computational screening and experimental validation.
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http://dx.doi.org/10.1002/adma.202416463 | 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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