ProteinIQ for virtual screening
Screen millions, discover one
Filter compound libraries at scale with QEPPI, EvoEF2, and PocketFlow to identify promising drug candidates efficiently.
Join 1,200+ researchers using ProteinIQ
01 — Challenges
Challenges we solve
Common obstacles researchers face and how ProteinIQ addresses them.
Screen large compound libraries efficiently
Identify druggable binding sites
Filter hits using molecular descriptors
Prioritize compounds for experimental validation
02 — Workflow
How it works
A streamlined workflow from input to results.
1
Load Library
Upload compound library or use built-in databases
2
Pre-filter
Apply drug-likeness and ADMET filters
3
Screen Compounds
Run QEPPI or molecular descriptor-based screening
4
Rank & Export
Analyze results and export top candidates
03 — Comparison
Traditional methods vs. ProteinIQ
See how we compare to traditional computational approaches.
| Aspect | Traditional | ProteinIQ |
|---|---|---|
| Infrastructure | Dedicated HPC cluster required | Serverless—scales on demand |
| Library size | Limited by local storage/compute | Screen millions of compounds |
| Time to results | Days to weeks for large libraries | Hours with parallel processing |
| Expertise required | HPC and cheminformatics skills | Intuitive web interface |
04 — Explore more
Related solutions
Discover complementary workflows to enhance your research.