Comparative analysis of machine learning methods in ligand-based virtual screening of large compound libraries.
Machine learning methods have been explored as ligand-based virtual screening tools for facilitating drug lead discovery. These methods predict compounds of specific pharmacodynamic, pharmacokinetic or toxicological properties based on their structure-derived structural and physicochemical properties. Increasing attention has been directed at these methods because of their capability in predicting compounds of diverse structures and complex structure-activity relationships without requiring the knowledge of target 3D structure. This article reviews current progresses in using machine learning methods for virtual screening of pharmacodynamically active compounds from large compound libraries, and analyzes and compares the reported performances of machine learning tools with those of structure-based and other ligand-based (such as pharmacophore and clustering) virtual screening methods. The feasibility to improve the performance of machine learning methods in screening large libraries is discussed.
Ma
Xiao H
XH
Bioinformatics and Drug Design Group, Department of Pharmacy and Center of Computational Science and Engineering, National University of Singapore, 3 Science Drive 2, Singapore. phacyz@nus.edu.sg
Jia
Jia
J
Zhu
Feng
F
Xue
Ying
Y
Li
Ze R
ZR
Chen
Yu Z
YZ
D001185:Artificial Intelligence; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004347:Drug Interactions; D008024:Ligands; D004364:Pharmaceutical Preparations / Q000138:chemical synthesis / Q000737:chemistry*; D054852:Small Molecule Libraries; D013329:Structure-Activity Relationship
D008024:Ligands; D004364:Pharmaceutical Preparations; D054852:Small Molecule Libraries