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Application of Random Forest Method to QSAR Model Building and Prediction of Toxicity

机译:随机森林法在QSAR模型建立和毒性预测中的应用

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@@ With growing environmental concern, a need for predicting the toxicity of compounds has emerged. Experimental assessment of toxicity can be costly, time consuming, and hazardous. Quantitative structure-activity relationships (QSARs) can be used to predict toxicity accurately based on experimentally known toxicities. QSARs modeling tools have traditionally been satisfied by the Statistics, Machine Learning methods. Considering the data dimension, descriptor selection, and prediction accuracy, Random Forest (RF) method was selected for the descriptor selection and model building in the present study.
机译:随着对环境的日益关注,已经出现了预测化合物毒性的需求。毒性的实验评估可能是昂贵,费时且危险的。定量构效关系(QSAR)可用于根据实验已知的毒性准确预测毒性。传统上,统计,机器学习方法可以满足QSAR建模工具的要求。考虑到数据维度,描述符选择和预测精度,本研究选择随机森林(RF)方法进行描述符选择和模型建立。

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