Polypharmacological Target Prediction?
Find common drug targets, enriched pathways, and interaction networks — starting from either a set of diseases or a set of genes
How to Use & Interpret Results ▾
How to use
- Choose your starting point: a set of Diseases, or a set of Genes.
- Search for names and move them into the "Selected" box using the → button (or double-click); use ← to remove one. Click Load Example to try it with sample data first.
- Pick a dataset: Curated (faster) or Curated + Text Mining (broader coverage, but computationally intensive — may take longer).
- Submit to run the analysis. If nothing is shared across your entire selection, you'll see a warning and the analysis won't proceed.
What you get
- Common Genes/Diseases: the genes (or diseases) shared by every item you selected, linked to their detail pages.
- Enriched Pathways: KEGG pathways significantly enriched in the common genes (Benjamini–Hochberg adjusted q < 0.05), with p-value, q-value and fold enrichment.
- Network Visualization: an interactive gene–pathway network graph you can pan and zoom.
- Network Analysis: a protein interaction network scored for degree, closeness and betweenness centrality, flagging hub and bottleneck genes (filterable via All/Hub/Bottleneck).
- Summary: hub & bottleneck genes cross-referenced with their pathways and known drugs.
- Drug Targets: known drug targets plus predicted ones, scored by sequence, functional, and network similarity.
- Drug Deep-Dive: pick any drug from Drug Targets and click "Analyze further" to pull together its clinical trial status, tissue expression (GTEx), transcriptomic cross-reference, side effects, and ADMET screening in one place.
- Every tab has a Download button to export its results.
