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🔒 AI-powered ADMET • Docking • Molecule generation

Radiant innovation for AI-driven drug discovery

RADGenT integrates in‑silico docking, cavity‑aware scoring, and ADMET prediction into one radiant platform.

Try the demoRequest access
🧪 Quick run: Docking + ADMET
Protein target
Example: 1HVR (HIV‑1 protease)
Ligand
Example: aspirin.smi
Scoring
Vina + cavity‑aware
ADMET
hERG / CYP / logS / HIA
✔️ Front‑end preview. Hook up your APIs to run real jobs.
⚙️ AutoDock Vina + cavity‑aware scoring
🧪 hERG, CYPs, solubility, permeability
📦 SDF, MOL2, PDBQT, SMILES
🔁 Reproducible pipelines

Key features

Cavity‑aware docking
Pocket detection and constraint‑guided scoring alongside Vina‑style terms.
ADMET predictions
Supervised models for ADME/Tox with clear descriptors.
GPU optional
Scale docking batches on CPU or GPU.
Transparent & auditable
Versioned datasets, model cards, exportable reports.

Try the live demo

Ligand docking + ADMET (mock)
How it works
  1. Upload a ligand (SMILES/SDF) and choose a target (PDB ID).
  2. Select docking engine and pocket options.
  3. Press Run to simulate (mocked here) and preview results.

This demo runs locally. Connect your server API to enable real jobs and persistent results.

Pipeline

1. Input
SMILES/SDF → standardize, protonate, 3D conformers
2. Dock
Vina / cavity constraints → ranked poses
3. Score
Rescoring + knowledge‑guided penalties
4. ADMET
hERG / CYP / HIA / logS
Bring your molecules. We’ll handle the rest.
GitHub

By submitting, you agree to be contacted about early access. We never sell your data.

RADPT – Rajpal Agrawal Drug Prediction Tool

Enter candidate molecules (Name + SMILES) to evaluate key drug-likeness properties.