Predict drug safety, assess toxicity risks, and analyze molecular properties in seconds. Our AI models for BBB permeability, hERG liability, and AMES mutagenicity help you make smarter decisions earlier in drug development.
Prediction Models
ADMET Analysis
Research Assistant
ML-powered tools for early-stage drug safety and ADMET prediction
Predict blood-brain barrier penetration for CNS drug candidates. Identify compounds likely to cross the BBB for neurological targets.
Assess cardiac safety risk by predicting hERG channel inhibition. Flag potential QT prolongation liabilities early in development.
Predict mutagenic potential with our AMES test model. Screen compounds for genotoxicity risk before costly in-vitro studies.
Calculate key molecular descriptors including LogP, TPSA, molecular weight, H-bond donors/acceptors, and Lipinski properties.
Chat with an AI trained on your uploaded documents. Ask questions about your research papers, patents, and scientific literature.
Get detailed prediction explanations with molecular evidence. Understand which structural features drive each safety assessment.
From molecule to prediction in three simple steps
Draw your molecule using our interactive sketcher or enter a SMILES string. Our system validates the structure and calculates molecular descriptors automatically.
Our machine learning models analyze your compound for BBB permeability, hERG liability, and AMES mutagenicity—delivering results in seconds.
Get probability scores with detailed molecular evidence explaining which structural features influence each prediction. Make informed decisions faster.
We're making AI-powered drug discovery accessible to researchers and pharmaceutical teams of all sizes. Our mission is to reduce the time and cost of early-stage drug development by providing instant, reliable ADMET predictions.
Built by scientists and AI engineers with deep expertise in computational chemistry and machine learning, our models are trained on curated datasets to deliver actionable insights. We help you identify promising candidates faster and flag safety concerns before they become costly failures.