scientific-pharos-targets

To retrieve druggability data for IDG protein targets, you query the Pharos GraphQL API to fetch TDL classifications, disease associations, and ligand activities, then parse and save the results into CSV files using Python.

TDL classification is a target development level categorization system used in drug discovery to prioritize protein targets by querying the Pharos GraphQL API for disease associations and ligand activity insights.

Yes, querying the Pharos GraphQL API requires a Python environment with the requests and pandas libraries installed to execute the queries, parse the response data, and save the output results.

Yes, you can use Pharos API data for drug repurposing by analyzing disease-target relationships and ligand activities to generate actionable targets for downstream drug discovery workflows.

To export Pharos GraphQL API responses to CSV files, you use the pandas library in Python to parse the JSON query results and save the target profiles, disease associations, and ligand activities into structured tabular formats.