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基于大规模机器学习方法的NLRP3抑制化合物的多项分类

Muhammad Ishfaq¹, Syed Zahid Ali Shah², Ijaz Ahmad³

¹College of Computer Science, Huanggang Normal University, Huanggang, 438000, China.

Molecular diversity

|July 7, 2023

概括

机器学习模型有效地对NLRP3抑制剂进行了分类,这对于天生的免疫研究至关重要. 通过处理数据不平衡,合成少数人过量采样技术 (SMOTE) 显著提高了模型准确性.

科学领域:

免疫学免疫学免疫学
计算化学计算化学
药理学药理学是指药理学的学科.

背景情况:

NLRP3炎症体在天生的免疫力中发挥着关键作用,并与各种疾病有关,包括自身免疫和炎症性疾病.
机器学习 (ML) 在制药研究中越来越多地用于药物发现和开发.
识别NLRP3抑制剂对于针对NLRP3介导疾病的治疗干预至关重要.

研究的目的:

应用机器学习方法来对NLRP3抑制剂进行多项分类.
评估数据不平衡的影响以及合成少数超标采样技术 (SMOTE) 在提高分类准确度方面的有效性.
开发和验证用于预测NLRP3抑制剂的定量结构-活性关系 (QSAR) 模型.

主要方法:

使用来自ChEMBL数据库 (版本 29) 的154个分子进行了定量结构-活性关系 (QSAR) 建模.
开发了多项分类模型,重点是使用SMOTE解决数据不平衡.
使用准确性和日志损失等指标评估模型性能,并分析接收器操作特征 (ROC) 曲线.

主要成果:

开发的QSAR分类模型实现了高精度,顶级模型的精度从0.86到0.99.
SMOTE显著提高了分类器对少数群体的敏感性,并提高了整体模型的准确性.
调整参数和处理不平衡数据导致ROC图片值的大幅改善.

结论:

QSAR分类模型展示了强大的统计性能和可解释性.
在基于ML的药物发现中,SMOTE是处理不平衡数据集的有效技术.
这些模型显示出对新型NLRP3抑制剂的快速查有很大的潜力.

关键词:

机器学习机器学习多项式是一个多项式.这是NLRP3抑制剂.在QSAR中使用QSAR.

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