ProteinIQ for drug discovery

From molecules to medicines faster

Design and validate drug candidates with integrated ADMET-AI predictions, docking simulations, and lead optimization workflows.

ADMET-AI prediction

Predict absorption, distribution, metabolism, excretion, and toxicity properties of compounds using advanced machine learning algorithms for drug development

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Toxicity prediction

Assess compound safety profiles including hepatotoxicity, cardiotoxicity, mutagenicity, and other adverse effects using predictive computational models

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Lipinski's rule of 5

Filter compound libraries for drug-like properties using molecular weight, lipophilicity, hydrogen bonding criteria for enhanced oral bioavailability

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What you can do

Explore the key capabilities and workflows enabled by our tools for from molecules to medicines faster.

Predict ADMET properties for drug candidates

Assess toxicity and safety profiles

Filter compounds for drug-like properties

Optimize lead compounds for bioavailability

Explore related solutions

Discover complementary workflows and tools to enhance your research.

Accelerate binding predictions

Screen protein-ligand interactions using AutoDock Vina, DiffDock, and DynamicBind through a unified cloud platform.

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Screen millions, discover one

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

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From molecules to medicines fasterFrom molecules to medicines faster

ADMET-AI prediction

Toxicity prediction

Lipinski's rule of 5

What you can do

Explore related solutions

Accelerate binding predictions

Screen millions, discover one

From molecules to medicines faster