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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Comparative Analysis of Machine Learning Methods in Ligand-Based Virtual Screening of Large Compound Libraries

Author(s): Xiao H. Ma, Jia Jia, Feng Zhu, Ying Xue, Ze R. Li and Yu Z. Chen

Volume 12 , Issue 4 , 2009

Page: [344 - 357] Pages: 14

DOI: 10.2174/138620709788167944

Price: $65

Abstract

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.

Keywords: Activator, adverse drug reaction, agonist, antagonist, compound, computer aided dug design, drug, drug discovery, inhibitor, molecule


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