RADGenT helps design and evaluate new chemical entities (NCEs) — from target identification through virtual hits, lead optimisation, and clinical evidence.
Modular apps that assist at every stage of drug discovery — from statistical analysis to ADMET prediction, docking, and beyond.
Classical biostatistics for medical and pharmacological research: t-tests, ANOVA, Chi-square, regression, survival analysis and more.
Draw or edit molecules on a full 2D structure editor, then predict drug-likeness, ADMET, toxicity alerts, solid-form stability and possible synthesis routes.
One-button, structure-based docking: paste a ligand, enter a target PDB ID, and RADock prepares the structure, finds the binding pocket automatically, and returns ranked poses in an interactive 3D viewer — no command line, no manual setup.
Your central, secured library of NCE candidates — every molecule analysed in RADPT can be saved here with its full ADMET/tox profile, organised into folders by parent molecule.
QSAR and deep learning (GNN) models to predict potency, selectivity and ADMET from structure.
Estimate environmental risk of drugs and metabolites: persistence, bioaccumulation, ecotoxicity.
MCQ practice tool covering pharmacokinetics, adverse drug reactions, and clinical trial phases.
A growing deck of pharmacology pearls — flip through one card at a time.
RADGenT tools support each stage of the journey.
Select disease pathways and biological targets. Validate using in-vitro and in-vivo experiments.
Use RADock, RAlib and ML models to generate new chemical entities that interact with the target.
Optimise potency and ADMET with RADPT and ML. Analyse preclinical data statistically using STMS.
Design and analyse clinical trials, compile evidence for regulators, and move NCEs toward market approval.
Collaborate on AI-powered drug discovery.
Researchers, clinicians, chemists and developers are welcome.
RADGenT