使用ML模型预测化学呼吸毒性的OPTUNA优化

IF 3 3区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Eman Shehab, Hamada Nayel, Mohamed Taha
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引用次数: 0

摘要

分子毒性预测是药物发现过程中的一个重要环节。它直接关系到医学的命运和人类的健康。本文提出了一种改进的化学呼吸毒性预测模型。它使用了分子描述符和基于不同机器学习算法的术语频率-逆文档频率(TF-IDF)模型的组合。为了解决类的不平衡,应用了SMOTE。使用分类器生成更好的系统需要适当的超参数调优。因此,我们调整了各种模型的超参数,并使用调整后的参数对模型进行训练。我们使用OPTUNA调优超参数。通过内部和外部验证来确认模型的性能。结果表明,采用随机森林方法的模型内部验证精度和AUC分别为88.6%和93.2%。对于外部验证,使用随机森林和梯度增强分类器的模型精度值为92.2%,AUC为97%。将这些结果与先前的研究结果进行比较,表明我们的模型比它们表现得更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
OPTUNA optimization for predicting chemical respiratory toxicity using ML models

Predicting molecular toxicity is an important stage in the process of drug discovery. It is directly related to medical destiny and human health. This paper presents an enhanced model for chemical respiratory toxicity prediction. It used a combination of molecular descriptors and term frequency – inverse document frequency (TF-IDF) based models with different machine learning algorithms. To address class imbalance, SMOTE is applied. Appropriate hyper-parameter tuning is required to generate a better system with a classifier. So, we adjusted the hyper-parameters of various models and used the adjusted parameters to train the model. We tuned hyper-parameters using OPTUNA. Internal and external validation were used to confirm the models’ performance. According to the results, the model’s internal validation accuracy and AUC using the random forest approach were 88.6% and 93.2%. For external validation, the model’s accuracy value using random forest and Gradient Boosting Classifier were 92.2% with AUC 97%. Comparing these results with previous studies shows that our model performs better compared to them.

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来源期刊
Journal of Computer-Aided Molecular Design
Journal of Computer-Aided Molecular Design 生物-计算机:跨学科应用
CiteScore
8.00
自引率
8.60%
发文量
56
审稿时长
3 months
期刊介绍: The Journal of Computer-Aided Molecular Design provides a form for disseminating information on both the theory and the application of computer-based methods in the analysis and design of molecules. The scope of the journal encompasses papers which report new and original research and applications in the following areas: - theoretical chemistry; - computational chemistry; - computer and molecular graphics; - molecular modeling; - protein engineering; - drug design; - expert systems; - general structure-property relationships; - molecular dynamics; - chemical database development and usage.
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