Artificial Intelligence Powered Insights into Nanotoxicology
Abstract
The application of nanomaterials in medicine necessitates a thorough assessment of their toxicity to ensure their safe use in living organisms. Advanced technologies like artificial intelligence (AI) and machine learning (ML) are instrumental in processing vast datasets in toxicology, encompassing toxicological databases and image-based screening results. Nanomaterials exhibit significant variability in their physical and chemical properties, making their toxicological assessment unique. The potential adverse effects of these materials, as they find new applications in the consumer market, raise crucial concerns in clinical settings. Understanding the toxicological mechanisms of these substances is vital. Traditionally, the pharmaceutical industry employs animal models to evaluate compound toxicity before human trials, guided by various regulatory legislations. Modern toxicology increasingly relies on computational methods. Machine learning techniques, especially decision tree algorithms, are pivotal for categorizing nanomaterials in nanotoxicology. These algorithms identify essential input parameters and extract meaningful information from extensive datasets. This overview emphasizes the role of AI algorithms in nanotoxicology, quantitative toxicology, nano data collection, predictive models, essential nanoparticle properties, image-based databases, and various challenges in the field.
Keywords:
Algorithm Models, Collection of Nano data, Machine learning, PBPK Models, QSAR for Nano toxicology, Toxicology
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References
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Kumar, R., & Saha, P. (2022). A review on artificial intelligence and machine learning to improve cancer management and drug discovery. International Journal for Research in Applied Sciences and Biotechnology, 9(3), 149-156. https://doi.org/10.31033/ijrasb.9.3.26
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T, S., Chakraborty, T. and Prasanna, P. (2024) “Artificial Intelligence Powered Insights into Nanotoxicology”, International Journal of Advancement in Life Sciences Research, 7(2), pp. 68-80. doi: https://doi.org/10.31632/ijalsr.2024.v07i02.005.
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