抗菌化合物的基于联体的预测:克服分子数据中的类不平衡
Yiheng Du1, Khandaker Asif Ahmed2, Himadri Shekhar Mondal3
1College of Science and Medicine, The Australian National University, Canberra, Australia.
Computational biology and chemistry
|November 20, 2025
概括
机器学习模型通过处理不平衡的数据,有效地预测新型抗生素. 选择性相似性方法显著提高了模型性能,超过了药物发现的先前研究.
科学领域:
- 药用化学 医学化学
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 泛耐药细菌的兴起需要新型抗生素的发现.
- 传统的抗生素查是耗时且低效的.
- 机器学习 (ML) 提供了一种有前途的方法,通过预测化合物活性来加速抗生素的发现.
研究的目的:
- 为解决抗生素发现的ML模型中的数据不平衡问题.
- 为经典和神经网络模型开发和评估基于选择性相似性的方法.
- 为了提高抗菌化合物的预测准确度.
主要方法:
- 使用了14,393种化合物的数据集,包括534种抗生素.
- 应用K-means集群和图形相似性测量用于经典ML模型 (随机森林,物流回归,SVM,决策树,XGBoost,KNN).
- 采用图形卷积网络 (GCN) 模型,采用基于选择性相似性的不足抽样技术.
主要成果:
- 在未充分采样数据上训练的模型与在原始或随机采样数据上训练的模型相比,显示出显著的性能改善.
- 该GCN模型实现了优异的性能,ROC-AUC为0.97和PRC-AUC为0.98.
- 随机森林和决策树模型分别显示了经典ML模型中最好和最差的性能.
结论:
- 选择性基于相似性的不足抽样有效地提高了针对抗生素发现的ML模型性能.
- 开发的方法和模型显示了与现有研究相比的显著改进,提供了更有效的药物查过程.
- 这些发现为未来的抗生素开发和查策略提供了有价值的框架.
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