MedGraph-Rubie

MedGraph-Rubie is an AI-powered platform designed to accelerate drug repurposing by integrating knowledge graphs, docking simulations, and machine learning. It identifies novel drug-target interactions, enhancing lead identification and optimization for more effective therapeutic discovery.

Why Choose MedGraph-Rubie

The platform revolutionizes drug repurposing by leveraging data and AI. It offers comprehensive screening of over 10 million compounds, including FDA-approved, investigational, and PubChem/ChEMBL compounds, against target proteins. With a 99.9% accuracy rate, it utilizes knowledge graph insights to uncover drug repositioning opportunities.

Core Features

• Comprehensive Screening: Screen a wide range of compounds for potential drug-target interactions.
• Knowledge Graph Insights: Use structured data repositories to find new drug applications and repositioning opportunities.
• AI-Driven Docking: Advanced computational docking algorithms ensure precision in lead identification.
• FEP-Enabled Optimization: Free Energy Perturbation simulations provide atomic-level accuracy for binding affinity predictions.
• Scalable and High-Performance: HPC-parallel computing allows efficient large-scale screening.

Multiple Screening Libraries

The platform expands drug discovery through diverse compound libraries, including FDA-approved drugs, investigational drugs, and publicly available chemical repositories like PubChem and ChEMBL. Users can also input proprietary or selected compound sets for specialized screening applications.

Databases Used

• DrugBank: A comprehensive resource with chemical, pharmacological, and clinical data on drugs.
• ClinicalTrials.gov: Provides insights into ongoing and completed clinical trials.
• BindingDB: Contains measured binding affinities between small molecules and proteins.
• Open Targets: Links genes, diseases, and drug interactions.
• Gene Expression: Supplies transcriptomic data for analyzing differential gene expressions.
• STRING Database: Provides protein-protein interaction networks.
• Research Literature: Extracts knowledge from scientific publications using NLP-based text mining.
• Kyoto Encyclopedia: Maps pathways to understand drug-induced biological responses.

Our Process

1. Data Ingestion & Curation: Collects and standardizes biological data from multiple sources.
2. Target-Based & Reverse Docking: Uses AI-powered docking algorithms for computational screening.
3. Post-Docking Refinement: Analyzes and optimizes docking results.
4. FEP Simulations: Provides precise binding affinity predictions using molecular dynamics.
5. Report Generation: Offers detailed analysis and visualization of results.

Key Applications

The platform transforms drug repurposing across therapeutic areas such as oncology, infectious diseases, rare diseases, and neurological disorders. It identifies new drug candidates, discovers antiviral and antibacterial applications, finds alternative therapies for rare conditions, and repurposes existing drugs for neurodegenerative diseases.

MedGraph-Rubie enhances R&D efficiency by cutting pre-clinical discovery time, reducing costs, and lowering failure risks. It provides high-throughput data and collaborates with leading pharma and biotech companies for impactful solutions.