MLViS: A Web Tool for Machine Learning-Based Virtual Screening in Early-Phase of Drug Discovery and Development
Virtual screening is an important step in early-phase of drug discovery process. Since there are thousands of compounds, this step should be both fast and effective in order to distinguish drug-like and nondrug-like molecules. Statistical machine learning methods are widely used in drug discovery studies for classification purpose. Here, we aim to develop a new tool, which can classify molecules as drug-like and nondrug-like based on various machine learning methods, including discriminant, tree-based, kernel-based, ensemble and other algorithms. To construct this tool, first, performances of twenty-three different machine learning algorithms are compared by ten different measures, then, ten best performing algorithms have been selected based on principal component and hierarchical cluster analysis results. Besides classification, this application has also ability to create heat map and dendrogram for visual inspection of the molecules through hierarchical cluster analysis. Moreover, users can connect the PubChem database to download molecular information and to create two-dimensional structures of compounds.
MLViS main paper: http://www.plosone.org/article/fetchObject.action?uri=info:doi/10.1371/journal.pone.0124600&representation=PDF
MLViS web-tool: http://www.biosoft.hacettepe.edu.tr/MLViS/
First, install following R packages:
install.packages("devtools")
require(devtools)
install_version("shiny", version = "0.10.1", repos = "http://cran.us.r-project.org")
install.packages("caret")
install.packages("Rcpi")
install.packages("ape")
install.packages("plyr")
install.packages("RWeka")
install.packages("rJava")
install.packages("ChemmineR")
install.packages("gplots")
Then, run following code in R console:
shiny::runGitHub("MLViS", "selcukorkmaz")