数学:QSAR中的深度学习方法用于雌激素受体α抑制剂
Rizki Triyani Pusparini1,2, Adila Alfa Krisnadhi1, Firdayani2
1Tokopedia-UI AI Center of Excellence, Faculty of Computer Science, Universitas Indonesia, Depok 16424, Indonesia.
Molecules (Basel, Switzerland)
|August 12, 2023
概括
这项研究引入了一种新的计算模型,用于预测雌激素受体α抑制剂用于乳腺癌治疗. MATH模型显示出高精度,有可能加速激素治疗的药物发现.
科学领域:
- 计算化学是一种计算化学.
- 在瘤学瘤学.
- 药理学 药理学是指药理学的学科.
背景情况:
- 乳腺癌是妇女死亡的主要原因,激素疗法以雌激素作为关键治疗向.
- 对雌激素受体α抑制剂的常规药物发现是耗时且昂贵的.
研究的目的:
- 开发和验证一种新的计算框架,用于预测雌激素受体α抑制剂.
- 为了利用定量结构-活性关系 (QSAR) 分析与机器学习相结合,用于药物发现.
主要方法:
- 利用监督学习整合自我注意力变压器和分子图信息.
- 开发了五种分类模型来预测雌激素受体α抑制剂.
- 评估模型性能使用精度,回忆,F1得分,特异性和ROC AUC.
主要成果:
- 拟议的MATH模型实现了高性能指标:精度 (0.952),回忆 (0.972),F1得分 (0.960),特异性 (0.922) 和ROC AUC (0.977).
- MATH对雌激素受体α抑制剂表现出强大的预测能力.
结论:
- MATH模型显示出有很大的潜力,可以帮助研究人员识别雌激素α抑制剂的候选化合物.
- 这种方法可以指导和加速乳腺癌治疗的药物发现途径.
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