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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model
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Toxicity prediction of small drug molecules of androgen receptor using multilevel ensemble model

机译：使用多级合奏模型的雄激素受体小药物分子的毒性预测

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摘要

In this study, efforts are created to develop a quantitative structure-activity relationship (QSAR)-based model, which are used for the prediction of toxicities to reduce testing in animals, time, and money in the early stages of drug development. An efficient machine learning model is developed to predict the toxicity of those drug molecules which binds to the androgen receptor (AR). Toxicity prediction is performed in terms of their activity, activity score, potency, and efficacy by using various physicochemical properties. A multilevel ensemble model is proposed, where its first level is performed ensemble-based classification of activity, and the second level is performed ensemble-based regression of activity score, potency, and efficacy of only those drug molecules which have been found active during the classification level. The AR dataset has 10,273 drug molecules where 461 are active, and 9812 are inactive, and each drug molecule has 1444 features. Therefore, our dataset is highly imbalanced having a very large number of features. Initially, we performed feature selection then the class imbalance problem is resolved. The k-fold cross-validation is accomplished to measure the consistency of the model. Finally, our proposed multilevel ensemble model has been validated and compared with some existing models.
机译：在这项研究中，创建努力来开发基于定量的结构 - 活动关系（QSAR）模型，用于预测毒性，以减少药物发育早期阶段的动物，时间和金钱中的测试。开发了一种有效的机器学习模型以预测与雄激素受体（AR）结合的那些药物分子的毒性。通过使用各种物理化学性质，在其活性，活动评分，效力和功效方面进行毒性预测。提出了一种多级集合模型，其中其第一级是基于基于集合的活动分类，并且第二级进行了基于基于活动分数，效力和仅在所发现的那些药物分子的活动分数的回归基于的活动分数，效力和功效。分类水平。 Ar DataSet具有10,273个药物分子，其中461是活性的，9812是无活性的，并且每个药物分子具有1444个特征。因此，我们的数据集具有非常多的功能，具有非常大量的功能。最初，我们执行了特征选择，然后解决了类别不平衡问题。完成k折叠交叉验证以测量模型的一致性。最后，我们建议的多级合奏模型已被验证并与某些现有模型进行了验证。

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