使用基于树的机器学习方法来选基于对接数据的亲和性
Hua Feng1, Fangyu Wang1, Ning Li2
1Henan Key Laboratory of Animal Immunology, Henan Academy of Agricultural Sciences, Zhengzhou, China.
Molecular informatics
|September 11, 2023
概括
机器学习,特别是C5.0决策树模型,使用虚拟对接数据有效选类药物候选者. 这种方法通过提高亲和力预测准确度来增强类药物发现.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 类药物发现依赖于识别高 afinity 的类.
- 机器学习为准确的酸亲和力查提供了一个强大的工具.
研究的目的:
- 用虚拟对接数据比较四种基于树的机器学习算法的性能,用于预测使用虚拟对接数据的化亲和力.
- 为了确定最优的算法,准确和强大的类亲和力预测.
主要方法:
- 四个基于树的算法 (CART,C5.0,BAG,RF) 应用于虚拟对接数据.
- 数据集使用缩放,集中和主要组件分析 (PCA) 进行了预处理.
- 模型性能使用准确度,卡帕,灵敏度,特异性,F1,MCC和AUC等指标进行评估.
主要成果:
- 优化的C5.0模型 (C50O) 在测试和未知数据集 (80.4%准确度) 上在多个指标上表现出卓越的性能.
- 袋式CART (BAG) 和优化的随机森林 (RFO) 与C50O具有很高的预测相关性,表明稳定性.
- 卡特 (CARTO) 在测试数据验证方面表现不佳,限制了其预测效用.
结论:
- C5.0决策树模型对于从虚拟对接数据中预测亲和力非常有效.
- 这项研究为基于树的模型在蛋白相互作用 (PPI) 研究中提供了一个基准.
- 这些发现支持扩大机器学习在开发疗法的应用.
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