混合类平衡方法用于化学化合物毒性预测
Felipe Santiago-Gonzalez1, Jose L Martinez-Rodriguez2, Carlos García-Perez3
1Multidisciplinary Academic Unit Reynosa-Rodhe, Autonomous University of Tamaulipas, Mexico.
混合类平衡通过解决不平衡的数据集,显著改善了计算药物毒性预测. 低采样技术被证明是最有效的,减少了样本重叠,并提高了模型性能超过10%.
科学领域:
- 计算化学是一种计算化学.
- 毒理学 毒理学 毒理学
- 机器学习 机器学习
背景情况:
- 在药物毒性预测中不平衡的数据集导致有偏见的计算模型.
- 解决数据不平衡对于准确和具有成本效益的毒性评估至关重要.
研究的目的:
- 评估混合类平衡技术,以改进计算毒性预测模型.
- 在Tox21数据集上评估不同平衡方案的性能.
主要方法:
- 化学结构 (SMILES) 转换为分子描述符 (MACCS,ECFP,Mordred).
- 应用个人 (过量采样/不足采样) 和混合 (基于比率的平衡) 方案.
- 在10个生物试验中评估了8种重新采样技术,6种描述器和5种分类模型.
主要成果:
- 与基线相比,所有类平衡方案的预测性能至少提高了10.01%.
- 使用MACCS-MLP的ENN技术提高了10.01%的性能.
- 与ECFP6-2048和MORDRED-XGB相结合的SMOTE (10%) 和RUS (90%) 分别实现了16.47%和22.62%的改善.
结论:
- 混合类平衡有效地提高了计算毒性预测的准确性.
- 低采样技术是特别有效的,因为显著的样本重叠.
- 拟议的方法提供了一个强大的方法来缓解毒性预测模型中的偏差.
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