Advanced Modeling Suite
Computational chemistry tools for drug discovery: molecular docking, dynamics simulations, pharmacophore modeling, and QSAR analysis
Molecular Docking Studio
Checking... Protein-Ligand Complex
Docking Parameters
Value: 8
Docking Results
Load protein & ligand, then run docking
Molecular Dynamics Simulator
Checking... Trajectory Viewer
Simulation Parameters
Analysis Metrics
Pharmacophore Builder
Checking... 3D Pharmacophore Model
Pharmacophore Features
H-Bond Donor
3
H-Bond Acceptor
2
Hydrophobic
4
Aromatic
2
Positive Charge
1
Negative Charge
0
QSAR Model Builder
Checking... Molecular Descriptors
CSV with 'smiles' and target property columns
Model Performance
R² Score
0.892
RMSE
0.234
MAE
0.156
Q² (LOO-CV)
0.845
Features Used
47 / 200
Virtual Screening Pipeline
Checking...
1
Select Protein Target
Choose the receptor for virtual screening
2
Build Compound Library
Select candidates to screen (click to toggle)
Aspirin
COX Inhibitor
MW: 180.2
Ibuprofen
NSAID
MW: 206.3
Caffeine
Stimulant
MW: 194.2
Imatinib
Kinase Inhibitor
MW: 493.6
Tamoxifen
ER Modulator
MW: 371.5
Metformin
AMPK Activator
MW: 129.2
Serotonin
Neurotransmitter
MW: 176.2
Penicillin G
Antibiotic
MW: 334.4
Lopinavir
Protease Inhibitor
MW: 628.8
Remdesivir
Antiviral
MW: 602.6
Dexamethasone
Corticosteroid
MW: 392.5
Chloroquine
Antimalarial
MW: 319.9
0 / 12 selected
3
Run Virtual Screening
Dock all selected compounds against the target
AI Drug Discovery Lab
Checking...REINVENT
Transformer + Policy Gradient for de novo molecular generation
MolDQN
Deep Q-Network for molecular optimization via atom-level edits
Rainbow DQN
Double DQN + PER + Dueling + Multi-step + Distributional + Noisy Nets
Epoch 0 / 1000
0%
Status: Idle
ETA: --
Training
1000
1e-3
RL Core
0.990
Network Architecture
3
0.10
Reward Weights
0.35
0.20
0.25
0.20
Sum: 1.00
Target Protein
0 reference compounds loaded
20 A
0.00
Every 20 epochs
Loss & Reward
Property Tracking
Generation Quality
Best vs Target
Top Generated Molecules
Best candidates discovered during training
Best candidates discovered during training
Start training to discover molecules