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A combined drug discovery strategy based on machine learning and molecular docking
Author(s) -
Zhang Yanmin,
Wang Yuchen,
Zhou Weineng,
Fan Yuanrong,
Zhao Junnan,
Zhu Lu,
Lu Shuai,
Lu Tao,
Chen Yadong,
Liu Haichun
Publication year - 2019
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/cbdd.13494
Subject(s) - virtual screening , adaboost , machine learning , computer science , gradient boosting , random forest , drug discovery , artificial intelligence , support vector machine , decision tree , boosting (machine learning) , ensemble learning , k nearest neighbors algorithm , data mining , bioinformatics , biology
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.