计算毒理学的研究趋势：文献计量分析
Research trends of computational toxicology: a bibliometric analysis.
作者信息
Yarmohammadi Fatemeh, Hayes A Wallace, Karimi Gholamreza
机构信息
Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
University of South Florida College of Public Health, Tampa, FL, USA and Institute for Integrative Toxicology, Michigan State University, East Lansing, MI, United States.
出版信息
Toxicol Res (Camb). 2024 Sep 21;13(5):tfae147. doi: 10.1093/toxres/tfae147. eCollection 2024 Oct.
BACKGROUND
Computational toxicology utilizes computer models and simulations to predict the toxicity of chemicals. Bibliometric studies evaluate the impact of scientific research in a specific field.
METHODS
A bibliometric analysis of the computational methods used in toxicity assessment was conducted on the Web of Science between 1977 and 2024 February 12.
RESULTS
Findings of this study showed that computational toxicology has evolved considerably over the years, moving towards more advanced computational methods, including machine learning, molecular docking, and deep learning. Artificial intelligence significantly enhances computational toxicology research by improving the accuracy and efficiency of toxicity predictions.
CONCLUSION
Generally, the study highlighted a significant rise in research output in computational toxicology, with a growing interest in advanced methods and a notable focus on refining predictive models to optimize drug properties using tools like pkCSM for more precise predictions.
背景
计算毒理学利用计算机模型和模拟来预测化学物质的毒性。文献计量学研究评估特定领域科学研究的影响力。
方法
于1977年至2024年2月12日在科学网对毒性评估中使用的计算方法进行了文献计量分析。
结果
本研究结果表明，多年来计算毒理学有了很大发展，朝着更先进的计算方法发展，包括机器学习、分子对接和深度学习。人工智能通过提高毒性预测的准确性和效率，显著增强了计算毒理学研究。
结论
总体而言，该研究突出了计算毒理学研究产出的显著增长，对先进方法的兴趣日益浓厚，并且特别注重使用诸如pkCSM等工具优化预测模型以优化药物特性，从而实现更精确的预测。
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