DrugBank Database XML Parser

Getting Started – Loading the Data

Following is sample code that parses the DrugBank database, then loads the drugs info, drug groups info and drug targets actions info.

## load dbparser package
library(dbparser)
library(dplyr)
library(ggplot2)
library(XML)

## parse data from XML and save it to memory
get_xml_db_rows("drugbank_database.xml")

## load drugs data
drugs <- parse_drug()

## load drug groups data
drug_groups <- parse_drug_groups()

## load drug targets actions data
drug_targets_actions <- parse_drug_targets_actions()

Saving into a database

The parsed data may be saved into a given database. Databases supported by dbparser include MS SQL Server, MySQL and any database supported by DBI package. Following is an example of saving the parsed data into a MySQL database.

## open a connection to the desired database engine with an already
## created database
open_db(xml_db_name =  "drugbank.xml", driver = "SQL Server",
server = "MOHAMMED\\\\SQL2016", output_database = "drugbank")

## save 'drugs' dataframe to DB
parse_drug(TRUE)

## save 'drug_groups' dataframe to DB
parse_drug_groups(TRUE)

## save 'drug_targets_actions' dataframe to DB
parse_drug_targets_actions(TRUE)

## finally close db connection
close_db()

Exploring the data

Following is an example involving a quick look at a few aspects of the parsed data. First we look at the proportions of biotech and small-molecule drugs in the data.

## view proportions of the different drug types (biotech vs. small molecule)
drugs %>% 
    select(type) %>% 
    ggplot(aes(x = type)) + 
    geom_bar() + 
    guides(fill=FALSE)     ## removes legend for the bar colors

Below, we view the different drug_groups in the data and how prevalent they are.

## view proportions of the different drug types for each drug group
drugs %>% 
    rename(parent_key = primary_key) %>% 
    full_join(drug_groups, by = 'parent_key') %>% 
    select(type, text) %>% 
    ggplot(aes(x = text, fill = type)) + 
    geom_bar() + 
    theme(legend.position= 'bottom', 
          axis.text.x = element_text(angle=45, vjust=0.5)) + 
    labs(x = 'Drug Group', 
         y = 'Quantity', 
         caption="created by ggplot") + 
    coord_flip()

Finally, we look at the drug_targets_actions to observe their proportions as well.

## get counts of the different target actions in the data
targetActionCounts <- 
    drug_targets_actions %>% 
    group_by(text) %>% 
    summarise(count = n()) %>% 
    arrange(desc(count))

## get bar chart of the 10 most occurring target actions in the data
p <- 
    ggplot(targetActionCounts[1:10,], 
           aes(x = reorder(text,count), y = count, fill = letters[1:10])) + 
    geom_bar(stat = 'identity') +
    labs(fill = 'action', 
         x = 'Target Action', 
         y = 'Quantity', 
         caption = 'created by ggplot') + 
    guides(fill = FALSE) +    ## removes legend for the bar colors
    coord_flip()              ## switches the X and Y axes

## display plot
p