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Research and development (R&D) of nanodrugs is a long, complex and uncertain process. Since the 1960s, computing has been used as an auxiliary tool in the field of drug discovery. Many cases have proven the practicability and efficiency of computing in drug discovery. Over the past decade, computing, especially model prediction and molecular simulation, has been gradually applied to nanodrug R&D, providing substantive solutions to many problems. Computing has made important contributions to promoting data-driven decision-making and reducing failure rates and time costs in discovery and development of nanodrugs. However, there are still a few articles to examine, and it is necessary to summarize the development of the research direction. In the review, we summarize application of computing in various stages of nanodrug R&D, including physicochemical properties and biological activities prediction, pharmacokinetics analysis, toxicological assessment and other related applications. Moreover, current challenges and future perspectives of the computing methods are also discussed, with a view to help computing become a high-practicability and -efficiency auxiliary tool in nanodrugs discovery and development.
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http://dx.doi.org/10.3390/pharmaceutics15041064 | DOI Listing |
Cell Rep Med
August 2025
Center for Biomedical-photonics and Molecular Imaging, Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province, School of Life Science and Technology, Xidian University, Xi'an, Shaanxi 710126, China; Engineering Research Center of Mole
Rapid identification and accurate diagnosis are critical for individuals with acute leukemia (AL). Here, we propose a combined deep learning and surface-enhanced Raman scattering (DL-SERS) classification strategy to achieve rapid and sensitive identification of AL with various subtypes and genetic abnormalities. More than 390 of cerebrospinal fluid (CSF) samples are collected as targets, encompassing healthy control, AL patients, and individuals with other diseases.
View Article and Find Full Text PDFFront Neurosci
August 2025
Department of First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, China.
Background: Depression is a common mental disorder, and its diagnosis is highly dependent on subjective assessment. Electroencephalogram (EEG), as a non-invasive and economical neurophysiological tool, has garnered considerable attention in recent years in the research of auxiliary diagnosis and clinical application. However, there exists a limited number of articles that summarize this body of research.
View Article and Find Full Text PDFJ Forensic Sci
September 2025
Laboratório de Ecologia Comportamental, Universidade Estadual de Mato Grosso do Sul (UEMS), Dourados, Mato Grosso do Sul, Brazil.
Blowflies are important to estimate the postmortem interval (PMI), since they are the first to interact with the carcass. However, depending on the decomposition stage, only pupae can be found. A method that has currently been suggested is the use of cuticular hydrocarbons (CHCs) in forensically important fly species to aid in estimating PMI; however, studies from the pupal stage are rare.
View Article and Find Full Text PDFNurse Educ Pract
September 2025
School of Nursing, Anhui Medical University, No.81 Meishan Road, Shushan District, Hefei, Anhui 230032, PR China; Department of Nursing, The First Affiliated Hospital of Anhui Medical University, No.218 Jixi Road, Shushan District, Hefei, Anhui 230022, PR China. Electronic address:
Aims: This study aimed to explore the effects of interactive teaching strategies based on generative artificial intelligence (GenAI) under the guidance of outcome-based education (OBE) theory on higher-order thinking skills (HOTS) and artificial intelligence (AI) literacy of undergraduate nursing students.
Background: Recently, GenAI-assisted teaching has been widely recognised as a trend in nursing education reform. HOTS and AI literacy are important for nursing students in the era of artificial intelligence.
Comput Med Imaging Graph
August 2025
Institute of Advanced Technology, Zhejiang University of Technology, Hangzhou, China. Electronic address:
The segmentation of cranial nerves (CNs) tract provides a valuable quantitative tool for the analysis of the morphology and trajectory of individual CNs. Multimodal CN segmentation networks, e.g.
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