数量结构-活动关系
化学信息学
水准点(测量)
计算机科学
预测建模
不良结局途径
领域(数学)
肺纤维化
计算模型
机器学习
过程(计算)
人工智能
纤维化
计算生物学
生物信息学
医学
生物
肺纤维化
数学
操作系统
地理
纯数学
病理
大地测量学
作者
Maciej Gromelski,Filip Stoliński,Karolina Jagiełło,Anna Rybińska-Fryca,Andrew Williams,Sabina Halappanavar,Ulla Vogel,Tomasz Puzyn
出处
期刊:Nanotoxicology
[Informa]
日期:2022-02-07
卷期号:16 (2): 183-194
被引量:1
标识
DOI:10.1080/17435390.2022.2064250
摘要
Nano-QSAR model allows for prediction of the toxicity of materials that have not been experimentally tested before by linking the nano-related structural properties with the biological responses induced by nanomaterials. Prediction of adverse effects caused by substances without having to perform time- and cost-consuming experiments makes QSAR models promising tools for supporting risk assessment. However, very often, newly developed nano-QSAR models are not used in practice due to the complexity of their algorithms, the necessity to have experience in chemoinformatics, and their poor accessibility. In this perspective, the aim of this paper is to encourage developers of the QSAR models to take the effort to prepare user-friendly applications based on predictive models. This would make the developed models accessible to a wider community, and, in effect, promote their further application by regulators and decision-makers. Here, we describe a web-based application that enables to predict the transcriptomic pathway-level response perturbated in the lungs of mice exposed to multiwalled carbon nanotubes. The developed application is freely available at http://aop173-event1.nanoqsar-aop.com/apps/aop_app. It requires only two types of input information related to analyzed nanotubes (their length and diameter) to assess the doses that initiate the inflammation process that may lead to lung fibrosis.
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