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Development of a QSAR model to predict hepatic steatosis using freely available machine learning tools.
Food and Chemical Toxicology ( IF 4.3 ) Pub Date : 2020-06-14 , DOI: 10.1016/j.fct.2020.111494
J Cotterill 1 , N Price 1 , E Rorije 2 , A Peijnenburg 3
Affiliation  

There are various types of hepatic steatosis of which non-alcoholic fatty liver disease, which may be caused by exposure to chemicals and environmental pollutants is the most prevalent, representing a potential major health risk. QSAR modelling has the potential to provide a rapid and cost-effective method to identify compounds which may trigger steatosis. Although models exist to predict key molecular initiating events of steatosis such as nuclear receptor binding, we are aware of no models to predict the apical effect steatosis. In this study, we describe the development of a QSAR model to predict steatosis using freely available machine learning tools. It was built using a dataset of 207 pharmaceuticals and pesticides which were identified as steatotic or non-steatotic from existing data from in vivo human and animal studies. The best performing model developed using the linear discriminant analysis module in TANAGRA, based on four chemical descriptors, had an accuracy of 70%, a sensitivity of 66% and a specificity of 74%. The expansion of the steatosis dataset to other chemical types, to enable the development of further models, would be of benefit in the identification of compounds with a range of mechanisms of action contributing to steatosis.



中文翻译:

使用免费的机器学习工具开发QSAR模型以预测肝脂肪变性。

肝脂肪变性有多种类型,其中最常见的是非酒精性脂肪肝疾病,这可能是由于接触化学物质和环境污染物引起的,这代表着潜在的重大健康风险。QSAR建模有可能提供一种快速且经济高效的方法来鉴定可能引发脂肪变性的化合物。尽管存在用于预测脂肪变性的关键分子引发事件(例如核受体结合)的模型,但我们知道尚无模型可以预测脂肪尖顶效应。在这项研究中,我们描述了使用免费的机器学习工具开发的QSAR模型来预测脂肪变性。它是使用207种药物和农药的数据集构建而成的,这些数据已从体内现有数据中鉴定为脂肪变性或非脂肪变性人类和动物研究。使用TANAGRA中的线性判别分析模块开发的性能最佳的模型(基于四个化学描述符)具有70%的准确度,66%的灵敏度和74%的特异性。将脂肪变性数据集扩展到其他化学类型,以便能够开发进一步的模型,将有助于鉴定具有多种导致脂肪变性的作用机理的化合物。

更新日期:2020-06-26
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