虚拟筛选
阿达布思
机器学习
计算机科学
梯度升压
随机森林
人工智能
决策树
药物发现
支持向量机
Boosting(机器学习)
集成学习
k-最近邻算法
数据挖掘
生物信息学
生物
作者
Yanmin Zhang,Yuchen Wang,Wei Zhou,Yuanrong Fan,Jing Zhao,Ling Zhu,Shan Lü,Tao Lü,Yadong Chen,Haichun Liu
摘要
Abstract Data mining methods based on machine learning play an increasingly important role in drug design and discovery. In the current work, eight machine learning methods including decision trees, k‐Nearest neighbor, support vector machines, random forests, extremely randomized trees, AdaBoost, gradient boosting trees, and XGBoost were evaluated comprehensively through a case study of ACC inhibitor data sets. Internal and external data sets were employed for cross‐validation of the eight machine learning methods. Results showed that the extremely randomized trees model performed best and was adopted as the first step of virtual screening. Together with structure‐based virtual screening in the second step, this combined strategy obtained desirable results. This work indicates that the combination of machine learning methods with traditional structure‐based virtual screening can effectively strengthen the ability in finding potential hits from large compound database for a given target.
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