Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.

Introduction: In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods: Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion: In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervision, balancing computing resources, and developing novel graph neural network models with better interpretability are the key technical issues currently faced. This paper analyzes the current state, hot spots, and trends of graph neural network applications in drug discovery through bibliometric approaches, as well as the current issues and challenges in this field. These findings provide researchers with valuable insights on the current status and future directions of this field.

Research Progress of Coronavirus Based on Bibliometric Analysis.

BACKGROUND: COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided. METHOD: the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis. RESULTS: 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the JournalofVirology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing. CONCLUSIONS: international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

Comparative efficacy and safety of multiple antiplatelet therapies for secondary prevention of ischemic stroke or transient ischemic attack: A network meta-analysis.

BACKGROUND: Antiplatelet therapies for secondary prevention of ischemic stroke or transient ischemic attack (TIA) is a highly active research topic with five critical drugs obtained by visual analysis. We aimed to compare and rank multiple antiplatelet therapies using a network meta-analysis. METHODS: Relevant medical databases were searched. Eligible randomized controlled trials (RCTs) which examined any comparisons involving mono- or dual antiplatelet therapies, based on aspirin, clopidogrel, dipyridamole, ticlopidine, cilostazol and placebo for patients with noncardioembolic ischemic stroke or TIA, were included. 14 outcomes were assessed. Primary outcomes were stroke recurrence, composite events (stroke recurrence, myocardial infarction and vascular death), and intracranial hemorrhage. PROSPERO registered number CRD42017069728. RESULTS: 45 RCTs with 173,131 patients were included in network meta-analysis, involving eight antiplatelet therapies. Cilostazol and clopidogrel were statistically more efficacious than aspirin (odds ratio (OR) = 0.64, 95% confidence interval (CI) = 0.47-0.88; OR = 0.77, 95%CI = 0.62-0.95) and dipyridamole (OR = 0.64, 95%CI = 0.44-0.93; OR = 0.76, 95%CI = 0.58-0.99) in reducing stroke recurrence, and showed significant benefits in reducing composite events compared with aspirin (OR = 0.63, 95%CI = 0.45-0.89; OR = 0.90, 95%CI = 0.83-0.97). No significant difference was found between cilostazol and clopidogrel in intracranial hemorrhage. Weighted regression suggested cilostazol was hierarchically the optimum treatment in consideration of both efficacy and safety, followed by clopidogrel. CONCLUSION: Cilostazol and clopidogrel are probably promising options for secondary prevention of ischemic stroke or TIA. Both of them reduce stroke recurrence similarly compared with aspirin or dipyridamole, and reduce composite events compared with aspirin. Further studies are needed to confirm this finding.