An advanced research companion that simulates drug interactions in real-time. Combines DeepChem predictive modeling with 3D Molecular Visualization to analyze how Dexamethasone competes with other agents at the glucocorticoid receptor.
Real-time PubChem Rendering
Built with Advanced Technologies
Integrated PubChem API to fetch and render interactive 3D crystallography models of drug interactions instantly in the browser using WebGL.
Uses a Random Forest classifier trained on the Tox21 dataset to predict receptor binding affinity and simulate competitive displacement risks (e.g., Dex vs. Cortisol).
Connected Google Gemini 1.5 Pro to translate complex pharmacodynamic data into "Patient-Friendly" or "Clinical Deep Dive" summaries on demand.
The system operates on a hybrid "Neuro-Symbolic" architecture. It combines deterministic biological rules (Expert Systems) with probabilistic machine learning models to ensure high accuracy for known interactions while maintaining predictive capability for novel compounds.
RDKit Featurization (ECFP4 Fingerprints) → Scikit-Learn Inference.
Dynamic DOM manipulation for Biological Flow Visualization.
def analyze_interaction(drug_a, drug_b):
# 1. Resolve Structures
smiles_a = resolve(drug_a)
smiles_b = resolve(drug_b)
...
# 2. Predict Competition
if model.predict(feat_a) == 1:
risk = "HIGH_COMPETITION"
return generate_3d_report(risk)