Getting Started: Table Builder
The DrugBank Table Builder has been intentionally designed to empower low- and no-code users to access the full extent of our biomedical data through simple-to-build data tables. We’ve created Table Builder to make it easier for you to gain valuable insights into drug discovery and development, and to recognize trends and unmet needs in the pharmaceutical industry.
Tables can be built by selecting one of our four starting points: Proteins, Drugs, Clinical Trials, or Diseases. You can add or remove information by adjusting the columns in your table, then, narrow the results using a set of predefined filters to build a table to suit your unique research interests. The Table Builder leverages DrugBank data quality and connectivity to provide robust and reliable results.
We have also integrated into the Table Builder a feature called Journal Reader, that allows users to pull additional data from PubMed using our proprietary AI. By toggling on the Journal Reader button, you will be able to get early insights into the latest research trends.
Below, we have included a set of quickstart guides that will guide you through each of the different types of tables that can be created using the Table Builder:
In this quickstart guide, we will dig into a fictional scenario that highlights the different functionalities and capabilities of the DrugBank Table Builder with Proteins as the starting point:
A research group is interested in the protein Akt as a drug target. Using a drug screening assay on different types of cancer cells, they identified several compounds that inhibit the three different Akt isoforms with varying strengths. Although this is early-stage research, they want to know more about other Akt inhibitors, their approval status, and their performance in clinical trials.
✅ Step 1: Create a table
In the Table Builder, we will create a new table by clicking the Create new table button and selecting Proteins from the drop-down menu.
✅ Step 2: Apply filters
The new table includes all of the data available in DrugBank. To narrow down the information we are interested in, use the Add Filter + button. For this specific scenario, we will filter out the three Akt isoforms we are interested in: Akt1, Akt2 and Akt3 (also known as RAC-alpha, RAC-beta and RAC-gamma serine/threonine-protein kinase), and use the Related Dugs > Target Actions > Action Type filter to only see the drugs that inhibit these proteins.
We can filter out these 3 proteins using UniProtKB accession numbers in the Identifiers > UniProtKB Accession filter or by typing in the name of each of them in the Protein Details > Protein Name filter and selecting a species in the Organism > Organism Name filter.
To see the drugs acting as Akt inhibitors, use the Related Drugs> Target Actions > Action Type filter and select “Includes Only” and “Inhibitor”.
Click the Apply Filters button to update the table. It will now only include Akt1, Akt2 and Akt3, as well as a list of drugs reported to inhibit these proteins.
✅ Step 3: Add columns
Since we are interested in the approval status and clinical trial performance of each of these drugs, we will add this information to our table using the Add Column button. Clicking the Add Column button will open a drop-down menu with a search bar on top.
For this specific scenario, the Approval Status, Approved Region, Clinical Trial Phase and Clinical Trials Why Stopped: Categorized columns include the information we are interested in.
To update the table layout, click the Apply Columns button.
Our data is interconnected, which will allow you to pull relevant information from other types of data using filters or adding new columns. Although we focused on protein information (Akt isoforms), we were also able to pull the list of drugs that inhibit each of the proteins in our table, see the clinical trials associated with them, as well as the reasons why some of them stopped.
✅ Step 4: Review the table and make adjustments
Our table is now showing only the data we are interested in. Also, notice how some of the columns have links to additional information. For the table created in this scenario, we can:
Click on the protein and drug names to access their corresponding DrugBank card
Open the drop-down menus in each of the clinical trial phases and Why Stopped categories to view the associated NCTs.
At the time of writing, only one of the drugs included in this list (capivasertib) was approved. There were also experimental drugs such as A-674563 that have not been evaluated in clinical trials. Since the research group in our scenario above is interested in learning more about drugs that have clinical data available, we can filter out experimental drugs with the Related Drugs> Related Drug Products > Approval Status filter and select “Does Not Include” and “Experimental”.
✅ Step 5: Explore new data using the Journal Reader
Toggling on the Journal Reader button will add machine-curated data to the table. New information will appear in purple, and columns where the feature is available will be labelled with the ✨ icon. In this scenario, toggling on the Journal Reader button will bring up additional Akt inhibitors to our list.
The Journal Reader also provides a four-point scale reading comprehension rating as well as links to the original PubMed papers where this information was pulled from.
✅ Step 6: Save the table
The Save button stores the parameters specified to build a table. This way, we can return to a table previously created without having to reselect filters and columns.
The research group has now gathered a wealth of highly specific, relevant and reliable information that, otherwise, would have taken them a lot longer to find. Using the Table Builder, they obtained a list of drugs that inhibit the different Akt inhibitors and learned about their approval status and their performance in clinical trials.
In this quickstart guide, we will dig into a fictional scenario that highlights the different functionalities and capabilities of the DrugBank Table Builder with drugs as the starting point:
A start-up focused on the development of best-in-class drugs is exploring the possibility of incorporating sodium-glucose cotransporter-2 (SGLT2) inhibitors into its pipeline. They want to learn about SGLT2 inhibitors that have not been approved, their performance in clinical trials, as well as some of their chemical properties.
✅ Step 1: Create a table
In the Table Builder, we will create a new table by clicking the Create new table button and selecting Drugs from the drop-down menu.
✅ Step 2: Apply filters
The new table includes all of the drugs available in DrugBank. To narrow down this list and to include only the drugs we are interested in, use the Add Filter + button. For this specific scenario, we will select only small molecules that act as SGLT2 inhibitors and have not been approved.
Select small molecule drugs using the Drug Details > Modality filter and then choose the “Small molecule” option. To include only SGLT2 targets, use the Related Proteins > Protein Details > Protein Name filter and select “Includes” and “Sodium/glucose cotransporter-2”. To select the specific type of action, use the Related Proteins > Target Actions > Action Type filter and select “Includes Only” and “Inhibitor”. Finally, use the Related Drugs Products > Approval Status filter and select “Does Not Include” and “Approved”.
Click the “Apply Filters” button to update the table. It will now include the subset of drugs we want to focus on.
✅ Step 3: Add columns
To learn about the performance of these drugs in clinical trials and get some of their chemical properties we will use the Add Column button. Clicking the Add Column button will open a drop-down menu with a search bar on top, making it easier to find each of these fields.
For this specific scenario, select the Clinical Trial Phase and Clinical Trials Why Stopped: Categorized columns to get more information about the clinical performance of these drugs. The Why Stopped Categories are a DrugBank feature that classifies the reasons available in clinical trial databases into a set of pre-defined categories.
Other columns that are relevant to this specific scenario include InChI, Aqueous Solubility (g/l) and Molecular Weight (Da).
To update the table layout, click the Apply Columns button.
Our data is interconnected, which will allow you to pull relevant information from other types of data using filters or adding new columns. We started looking for drug-target relationships and we are now linking it to clinical trial data to get a full view of the drugs we are interested in.
✅ Step 4: Review the table and make adjustments
Our table is now showing only the data we are interested in. Also, notice how some of the columns have links to additional information. For the table created in this scenario, we can:
Click on the drug names and IDs to access the DrugBank card available for each of them
Open the drop-down menus in each of the clinical trial phases and Why Stopped categories to view the associated NCTs
At the time of writing, there were four non-approved SGLT2 inhibitors in our knowledgebase, with an extra one found using the Journal reader feature.
✅ Step 5: Explore new data using the Journal Reader
Toggling on the Journal Reader button will add machine-curated data to your table. New information will appear in purple, and columns where the feature is available will be labelled with the ✨ icon. In this scenario, toggling on the Journal Reader button will bring up additional SGLT2 inhibitors to our list.
The Journal Reader also provides a four-point scale reading comprehension rating as well as links to the original PubMed papers where this information was pulled from.
✅ Step 6: Save the table
The Save button stores the parameters specified to build a table. This way, we can return to a table previously created without having to reselect filters and columns.
The information obtained using the Table Builder can help the researchers in this scenario to analyze some of the potential risks involved in the development of SGLT2 inhibitors. It also provided them with a succinct list of clinical trials that otherwise would have required a significant time investment.
In this quickstart guide, we will dig into a fictional scenario that highlights the different functionalities and capabilities of the DrugBank Table Builder with Clinical Trials as the starting point:
A pharmaceutical company interested in rare disease drug development is evaluating which specific disease they should focus on. One of the several candidates is cystic fibrosis, so they want to learn about the reasons why phase 2 and 3 clinical trials targeting this condition are stopped. To complete their analysis, they will also need additional details such as the drugs evaluated and the names of the companies leading those trials.
✅ Step 1: Create a table
In the Table Builder, we will create a new table by clicking the Create new table button and selecting Clinical Trials from the drop-down menu.
✅ Step 2: Apply filters
The new table includes all of the clinical trials available in DrugBank. To narrow down the information we are interested in, use the Add Filter + button. For this specific scenario, we will select phase 2 and 3 clinical trials focused on cystic fibrosis that have been halted.
Use the Trial Details > Condition filter and select “Includes” and “Cystic Fibrosis (CF)” to pull the clinical trials relevant to that condition. To view halted clinical trials, use the Trial Details > Trial Status filter, select “Includes Any” and check the boxes for “Suspended”, “Withdrawn” and “Terminated”. To focus only on phase 2 and 3 clinical trials, use the Trial Details > Trial Phase filter, select “Includes Any” and list both “Phase 2” and “Phase 3”.
Click the “Apply Filters” button to update the table. It will now include a list of phase 2 and 3 clinical trials focused on cystic fibrosis that were stopped at different points.
✅ Step 3: Add columns
To include the reason why these clinical trials were stopped and to associate each of them with their sponsor, we will use the Add Column button. Clicking the Add Column button will open a drop-down menu with a search bar on top where we can select additional information for our table.
When researchers provide the reasons why a clinical trial was stopped, they give a general description. To make this information easier to process, we created Why Stopped Categories that classify the reasons provided by researchers into a set of pre-defined categories. Both the Why Stopped: Categorized and Why Stopped: Uncategorized columns are available in the Table Builder.
For this specific scenario, we will select the Why Stopped: Categorized, Company Trial Sponsor: Normalized, Start Date, End Date, and Related Drugs > Drug Name columns to include the information we are interested in.
To update the table layout, click the Apply Columns button.
✅ Step 4: Review the table and make adjustments
Our table is now showing only the data we are interested in. Also, notice how some of the columns have links to additional information. For the table created in this scenario, we can:
Click on the drug names to access their corresponding DrugBank card
Click on each NCT to view all of the details associated with each clinical trial
At the time of writing, the most common reason why cystic fibrosis phase 2 and phase 3 clinical trials were stopped was due to recruitment issues. This is a known issue for rare diseases. Some of the other main reasons included funding, business decisions, and challenges due to the COVID-19 pandemic.
✅ Step 5: Save the table
The Save button stores the parameters specified to build a table. This way, we can return to a table previously created without having to reselect filters and columns.
The pharmaceutical company in this scenario has now gathered valuable information about the development of drugs used to treat cystic fibrosis. Using the Table Builder, they were also able to pull the names of the companies leading those trials as well as the dates they took place. Following a similar approach, they could now explore the results of successful clinical trials, or evaluate the potential of other rare diseases they may be interested in.
In this quickstart guide, we will dig into a fictional scenario that highlights the different functionalities and capabilities of the DrugBank Table Builder with Diseases as the starting point:
A tumor necrosis factor (TNF) inhibitor used for the treatment of different types of arthritis and psoriasis is losing patent protection in a few years. The company that owns this drug wants to explore all the diseases in which TNF is involved, to see if the same drug can be used for the treatment of other immune system diseases, and to check whether or not there have been previous drug development efforts.
✅ Step 1: Create a table
In the Table Builder, we will create a new table by clicking the Create new table button and selecting Diseases from the drop-down menu.
✅ Step 2: Apply filters
The new table includes all of the diseases that have been recorded in DrugBank, as well as their association with external ontologies. To narrow down this list, use the Add Filter + button. For this specific scenario, we will select immune system diseases associated with TNF.
Use the Target Disease Associations > Protein Name filter and select “Includes” and “Tumor necrosis factor” to pull the diseases associated with this protein. To view only diseases of the immune system, use the Disease Details > Disease Areas filter and select “Includes” and “Immune System Diseases”.
Click the “Apply Filters” button to update the table. It will now include a list of immune system diseases associated with TNF.
✅ Step 3: Add columns
To include additional information about drug development efforts performed for each target-disease pair, we will use the Add Column button. Clicking the Add Column button will open a drop-down menu with a search bar on top where we can select additional information for our table.
For this specific scenario, we will select the Target Disease Associations > Developmental Milestone column to include the highest milestone for a target-disease pair based on clinical or regulatory progress.
To update the table layout, click the Apply Columns button.
✅ Step 4: Review the table and make adjustments
Our table is now showing only the data we are interested in. Also, notice how some of the columns have links to additional information. For the table created in this scenario, we can:
Click on the condition name or ID to access DrugBank condition cards
Click on the protein name to access DrugBank protein cards
At the time of writing, there were 32 diseases associated with TNF in our system, and of those, 14 were immune system conditions. There were 3 targets with approved drugs for immune system conditions, while the rest of them had reached different stages of clinical research.
✅ Step 5: Save the table
The Save button stores the parameters specified to build a table. This way, we can return to a table previously created without having to reselect filters and columns.
The company that owns the TNF inhibitor that will soon lose patent protection now has a starting point with the different diseases they could explore to extend the drug’s patent life with new indications. Having information about the developmental milestones reached for each target-disease association helps them to easily discern between their different options.