A combined drug discovery strategy based on machine learning and molecular docking. (7th March 2019)

• Record Type: Journal Article
• Authors: Zhang, Yanmin; Wang, Yuchen; Zhou, Weineng; Fan, Yuanrong; Zhao, Junnan; Zhu, Lu; Lu, Shuai; Lu, Tao; Chen, Yadong; Liu, Haichun
• Is Part Of: Chemical biology & drug design. Volume 93:Number 5(2019)
• Page Start: 685
• Page End: 699
• Publication Date: 2019-03-07
• Subjects: ACC inhibitors -- extremely randomized trees -- machine learning -- molecular docking; Drugs -- Design -- Periodicals; Pharmaceutical chemistry -- Periodicals; Biochemistry -- Periodicals; 615.19005
• DOI: 10.1111/cbdd.13494
• Languages: English
• ISSNs: 1747-0277

Abstract

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. Abstract : Machine learning methods were applied to drug discovery through a case study of ACC inhibitors.