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Research trends of computational toxicology: a bibliometric analysis
Fatemeh Yarmohammadi1, A Wallace Hayes2, Gholamreza Karimi3,4
1Medical Biology Research Center, Health Technology Institute, Kermanshah University of Medical Sciences, Kermanshah, Iran.
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.
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