ProteinIQ for virtual screening

Screen millions, discover one

Filter compound libraries at scale with QEPPI, EvoEF2, and PocketFlow to identify promising drug candidates efficiently.

AutoDock-GPU

GPU-accelerated docking for high-throughput virtual screening. Screen thousands of compounds in minutes.

GNINA

CNN-enhanced docking with ML scoring. Higher accuracy for large-scale screening campaigns.

ADMET-AI

Pre-filter compounds by predicted ADMET properties. Eliminate poor candidates early in screening.

QEPPI

ML-based PPI inhibition prediction. Identify compounds that disrupt protein-protein interactions.

Lipinski's rule of 5

Filter compounds for drug-like properties. Essential pre-screening for oral bioavailability.

Molecular descriptors

Calculate 200+ molecular properties for QSAR modeling and ML-based virtual screening.

Why researchers choose ProteinIQ

Cloud-based screening scales automatically—no cluster needed

Screening millions of compounds requires dedicated HPC infrastructure

Multi-stage filtering with ML scoring improves hit quality

High false positive rates waste experimental resources

Built-in analytics and visualization for result interpretation

Managing and analyzing large result sets is overwhelming

Accessible screening tools with transparent per-job pricing

Commercial screening platforms cost tens of thousands annually

How it works

Large-scale virtual screening of compound libraries using advanced algorithms like QEPPI, EvoEF2, and PocketFlow. Identify promising drug candidates from millions of compounds.

Load library

Upload compound library or use built-in databases

Pre-filter

Apply drug-likeness and ADMET filters

Screen compounds

Run QEPPI or molecular descriptor-based screening

Rank & export

Analyze results and export top candidates