Stage 1
Dataset
Curated targets, receptors, and molecule libraries
Preparing QuDrugForge workspace
Preparing QuDrugForge workspace
PLEASE WAIT WHILE WE PREPARE THE WORKSPACE
Quantum AI Drug Discovery
Move from target evidence to defensible candidates.
QuDrugForge unifies patient-informed target selection, molecular generation, docking, exploratory quantum analysis, ADMET, simulation, and research-ready evidence in one governed workspace.
Target: EGFR Kinase Domain (Oncology)
Screening Status
GNINA + VQE RerankingActive Pipeline
Docking Score
-11.4 kcal/mol
ADMET Pass Rate
98.2%
Evidence boundaryResearch use
Wet-lab validation required
Execution recordsTraceable
Per-run provenance
Model statusVerified
Ready, absent, or quarantined
FallbacksDisclosed
Never silently substituted
Quantum roleExploratory
Zero decision weight by default
OutputsExportable
Reports and evidence artifacts
System architecture
Scientific Workflow
Every hypothesis advances through a visible scientific chain, with project inputs, compute jobs, and returned evidence connected at each stage.
Stage 2
Fingerprints
Structure-derived molecular feature space
Stage 3
Screening
QED, ADMET, and property constraint triage
Stage 4
Docking
Receptor-pocket binding pose generation
Stage 5
GNINA
CNN pose scoring and refinement
Stage 6
Quantum
Descriptor-assisted candidate reranking
Stage 7
Validation
Evidence review and decision packaging
One connected research system
Platform Core Capabilities
An end-to-end stack designed for traceable computational oncology, from molecular inputs to reviewable evidence.
01
3D Molecular Viewer
Inspect computed poses, receptor pockets, contacts, and stored structural artifacts.
02
Similarity Search
Compare project compounds with molecular fingerprints and transparent similarity scores.
03
Candidate Ranking
Prioritize candidates with recorded docking, GNINA, quantum, and ADMET evidence.
04
Experiment Tracking
Follow each scientific run through its status, logs, artifacts, and reproducible inputs.
05
Exploratory Quantum Scoring
Compare labeled quantum-assisted descriptors with classical baselines; no hardware-advantage claim.
06
AI Research Copilot
Navigate project evidence and research workflows through a focused assistant.
07
Pose Relaxation Review
Review recorded pose-relaxation jobs and their limits without presenting them as production complex MD.
08
Explainable Predictions
Keep model outputs connected to property evidence, provenance, and confidence.
Binding Pose Analysis
EGFR · refined pose 01
Illustrative receptor-pocket rendering showing the interaction evidence produced by the platform viewer.
Structure intelligence
Scientific Visualization
Inspect receptor pockets, refined ligand poses, labeled pose-relaxation evidence, and quantum-assisted evidence without losing the underlying provenance.
- GNINA pose refinement
- Protein-ligand contacts
- Pose-relaxation traces
- Quantum evidence surfaces
Oncology focus
Oncology Targets
Structure-guided workflows for high-impact, clinically established cancer targets.
Target diagrams are scientific illustrations; project workspaces use uploaded or registered receptor structures.
EGFR
Receptor tyrosine kinase
Epidermal Growth Factor Receptor
A central target in non-small cell lung cancer, including activating and resistance mutations.
Research contextL858R · T790M
PARP1
DNA repair enzyme
Poly [ADP-ribose] polymerase 1
A validated synthetic-lethality target in BRCA-altered ovarian, breast, and prostate cancers.
Research contextBRCA1/2 context
PIK3CA
Lipid kinase
Phosphatidylinositol 3-kinase alpha
A frequently altered oncogenic driver across breast, colorectal, and endometrial cancers.
Research contextE545K · H1047R
The Research Stack
RDKitCheminformatics
GNINADocking/Scoring
OpenMMMolecular Dynamics
QiskitQuantum Computing
FastAPIInference API
MongoDBApplication Data
PyTorchDeep Learning
Simple pricing
Start small. Scale when the science demands it.
Transparent plans for independent researchers, discovery teams, and regulated organizations.
Learn and explore
Free
For individual researchers evaluating the core discovery workflow.
₹0.00/ month
No credit card required
Single-user workspace
- ✓Up to 100 molecules per research run
- ✓Medchem, ADMET, and applicability triage
- ✓Reports with explicit evidence boundaries
- ✓No live docking or quantum-chemistry quota
- ✓Non-executing demos and cached evidence views
Independent research
Explorer
For scientists moving from early ideas into repeatable experiments.
₹5,200/ month
Billed ₹62,400 yearly
Single-user scientific tier
- ✓Up to 1,000 molecules per run
- ✓500 docking pairs per month
- ✓50 quantum-chemistry rows per month
- ✓Activity, ADMET, and evidence reporting
- ✓Standard email support
Most popular
Research
For active discovery teams running end-to-end candidate programs.
₹20,000/ month
Billed ₹240,000 yearly
Small-team scientific tier
- ✓Up to 10,000 molecules per run
- ✓5,000 docking pairs per month
- ✓500 quantum-chemistry rows per month
- ✓Exploratory QML with classical ablations
- ✓Research workspace collaboration
Multi-program teams
Scale
For growing biotech teams coordinating multiple discovery programs.
₹53,600/ month
Billed ₹643,200 yearly
Team-workspace scientific tier
- ✓Up to 100,000 molecules per run
- ✓50,000 docking pairs per month
- ✓5,000 quantum-chemistry rows per month
- ✓Higher-priority scientific job queue
- ✓Workflow integration scoping
Private and governed
Enterprise
For regulated organizations requiring tailored capacity and controls.
Custom
Tailored to your deployment
Capacity defined by contract
- ✓Custom compute and storage limits
- ✓Private cloud or on-premise deployment
- ✓SSO, SCIM, and audit implementation assessment
- ✓Custom model and data integrations
- ✓Security and procurement review
Prices exclude applicable taxes. Compute overages are billed separately.
INR uses localized plan pricing.