# Unlocking DrugBank: Parsing and Visualizing Mechanistic Data

#### Mohammed Ali

#### 2026-01-08

## Introduction

The DrugBank database is the global gold standard for drug information, containing rich data on chemical structures, pharmacology, and molecular targets. However, the raw data is provided as a massive, complex XML file that is difficult to use in statistical analysis.

`dbparser` solves this problem. It transforms that complex XML into a Drugverse Object (`dvobject`)—a structured collection of R data frames ready for analysis.

In this tutorial, we will demonstrate how to:

1. Parse the database.
2. Explore the data structure.
3. Visualize key insights using `dplyr` and `canvasXpress`.

## 1. Loading Data

### The Parsing Workflow

In a real-world scenario, you would parse the full XML database (downloaded from DrugBank) using `parseDrugBank()`.

    library(dbparser)

    # Parse the full XML file (this creates a dvobject)
    # drugbank_db <- parseDrugBank("drugbank_full_database.xml")

### Loading Sample Data

For this vignette, we will load curated sample datasets included in the package. These represent specific tables that would be found inside the full `dvobject`.

    suppressPackageStartupMessages({
      library(tidyr)
      library(dplyr)
      library(tibble)
      library(canvasXpress)
      library(dbparser)
    })

    # Load sample tables representing parts of the parsed dvobject
    drugs <- readRDS(system.file("drugs.RDS", package = "dbparser"))
    drug_groups <- readRDS(system.file("drug_groups.RDS", package = "dbparser"))
    drug_targets_actions <- readRDS(system.file("targets_actions.RDS", package = "dbparser"))

## 2. Analysis: The Drug Landscape

What does the universe of approved drugs look like? Let’s analyze the composition of the database by drug type (Small Molecule vs. Biotech).

    # Prepare data: Count drugs by type
    type_stat <- drugs %>%
      group_by(type) %>%
      summarise(Count = n()) %>%
      arrange(desc(Count)) %>%
      column_to_rownames("type")

    # Visualize
    canvasXpress(
      data             = type_stat,
      graphType        = "Bar",
      title            = "Composition of DrugBank: Drug Types",
      showSampleNames  = FALSE,
      legendPosition   = "right"
    )

## 3. Analysis: Approval Status

Drugs are categorized into groups such as “approved”, “investigational”, or “experimental”. How does the complexity (Biotech vs. Small Molecule) differ across these groups?

    # Prepare data: Cross-tabulate Type vs Group
    group_stat <- drugs %>%
      full_join(drug_groups, by = "drugbank_id") %>%
      group_by(type, group) %>%
      summarise(count = n(), .groups = 'drop') %>%
      pivot_wider(names_from = group, values_from = count, values_fill = 0) %>%
      column_to_rownames("type")

    # Visualize with a Stacked Bar Chart
    canvasXpress(
      data           = group_stat,
      graphType      = "Stacked",
      graphOrientation = "horizontal",
      title          = "Drug Types by Approval Status",
      xAxisTitle     = "Number of Drugs",
      legendPosition = "bottom",
      xAxis2Show     = FALSE
    )

## 4. Analysis: Molecular Mechanisms

One of DrugBank’s most valuable features is the detailed information on how drugs interact with their targets (Proteins, Enzymes, etc.). Are drugs mostly inhibitors, agonists, or antagonists?

    # Prepare data: Top 10 most common Mechanisms of Action
    targetActionCounts <- drug_targets_actions %>%
        group_by(action) %>%
        summarise(Count = n()) %>%
        arrange(desc(Count)) %>%
        slice_head(n = 10) %>%
        column_to_rownames("action")

    # Visualize
    canvasXpress(
      data            = targetActionCounts,
      graphType       = "Bar",
      graphOrientation = "vertical",
      colorBy         = "Count",
      title           = "Top 10 Mechanisms of Action",
      xAxisTitle      = "Number of Interactions",
      showSampleNames = FALSE,
      legendPosition  = "none"
    )

## 5. Next Steps: Integrated Pharmacovigilance

Now that you have mastered the mechanistic data in DrugBank, you can combine it with real-world data!

`dbparser` now supports OnSIDES (Adverse Events) and TWOSIDES (Drug-Drug Interactions).

Check out the Integrated Pharmacovigilance Vignette to learn how to merge these databases to perform polypharmacy risk analysis.