A combined drug discovery strategy based on machine learning and molecular docking

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 bo...

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Bibliographic Details
Published in:Chemical biology & drug design 2019-05, Vol.93 (5), p.685-699
Main Authors: Zhang, Yanmin, Wang, Yuchen, Zhou, Weineng, Fan, Yuanrong, Zhao, Junnan, Zhu, Lu, Lu, Shuai, Lu, Tao, Chen, Yadong, Liu, Haichun
Format: Article
Language:English
Subjects:
ACC inhibitors
Area Under Curve
Binding Sites
Crystallography, X-Ray
Databases, Factual
Drug Discovery
extremely randomized trees
Inhibitory Concentration 50
Machine Learning
molecular docking
Molecular Docking Simulation
Principal Component Analysis
Protein Structure, Tertiary
ROC Curve
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Summary: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. Machine learning methods were applied to drug discovery through a case study of ACC inhibitors.
ISSN:1747-0277
1747-0285
DOI:10.1111/cbdd.13494