Unlock More: Table Builder

Choosing a Table Builder Focus to Fit Your Needs

The Table Builder focus represents an essential component of the drug development process. The current version of the DrugBank Table Builder includes four options:

• Proteins: Complex molecules that commonly act as drug targets

• Drugs: Therapeutic compounds designed to treat diseases

• Clinical Trials: Studies evaluating novel treatments

• Diseases: Medical conditions with defined signs and symptoms

Once a focus is selected, the Table Builder will create a table that lists all of its respective data points. For example, a table focused on clinical trials will include all trials. From here, tables can be narrowed down using filters and new information can be incorporated by adding new columns. Therefore, selecting a focus allows users to tailor the layout of their tables and choose from different starting points.

For each focus, there is flexibility in the type of information that can be used as a starting point. For instance, the name of the gene that codes a specific protein or the protein domain can be used instead of the protein name.

Integrating and exploring drug development data

To provide users with actionable results, the DrugBank Table Builder was built based on three core components:

1. Data Interconnection: By linking essential data components through shared attributes, one can explore relationships such as:

   • Clinical trials run by a specific company

   • Drugs targeting a particular condition

   • Proteins affected by off-target drug effects

2. Enabling Exploration: Core concepts facilitate filtering and querying based on specific parameters, such as:

   • Identifying drugs that have off-target effects on proteins containing a specific domain (e.g., kinases)

   • Exploring clinical trials associated with a particular drug or disease

3. Driving Insights: This framework enables data-driven decision-making by uncovering patterns, interactions, and opportunities in drug development

The DrugBank Table Builder enables users to interrelate and filter data dynamically. For example, it can help with:

• Investigating clinical trials linked to a company provides insights into its research focus and pipeline strength

• Exploring drugs with off-target effects on proteins containing a specific domain can lead to new therapeutic opportunities or risk assessments

• Analyzing diseases associated with specific drugs reveals treatment landscapes and unmet needs