MedGraph - Rubie

MedGraph - Rubie™ is an AI-powered drug repurposing platform developed by Medvolt.ai that integrates knowledge graphs, molecular docking simulations, and machine learning to identify novel drug-target interactions. Designed for pharmaceutical and biotech researchers, it enables comprehensive compound screening across millions of molecules, accelerating lead identification and optimization for faster, more effective therapeutic discovery.

The platform is capable of screening over 10 million compounds, delivering knowledge graph insights with 99.9% accuracy, operating up to 100x faster than conventional approaches through AI-driven docking, and achieving 95% precision in binding affinity predictions via Free Energy Perturbation (FEP) simulations. High-performance computing (HPC) parallel infrastructure ensures scalable, large-scale screening is available around the clock.

Core Discovery Approaches

• Knowledge Graph-Based Discovery: Integrates FDA-approved drugs, clinical trial data, and research literature, using AI-powered extraction to predict new drug applications and repositioning opportunities from structured biological databases.
• Traditional Docking Approach: Advanced computational docking algorithms for precision lead identification against target proteins.
• Reverse Docking Approach: Screens compounds against multiple target proteins to uncover unexpected therapeutic applications.

Multiple Screening Libraries

• FDA-Approved Drug Library: A curated collection of drugs already approved for human use, enabling rapid clinical translation.
• Investigational Drug Library: Experimental compounds currently in clinical trials with known pharmacokinetic and safety profiles.
• PubChem & ChEMBL Compound Libraries: Publicly available chemical repositories containing millions of bioactive molecules.
• Custom Screening Libraries: Users can input proprietary or selected compound sets for specialized screening applications.

Integrated Databases

• DrugBank: Comprehensive chemical, pharmacological, and clinical data on drugs.
• ClinicalTrials.gov: Real-time insights into ongoing and completed clinical trials, linking investigational drugs to diseases.
• BindingDB: Measured binding affinities between small molecules and proteins for drug-target interaction studies.
• Open Targets: Links genes, diseases, and drug interactions to enhance disease-target mapping.
• Gene Expression Data: Transcriptomic data for analyzing differential gene expressions related to diseases and treatments.
• STRING Database: Protein-protein interaction networks supporting target validation for repurposed drugs.
• Research Literature: NLP-based text mining of peer-reviewed scientific publications for knowledge extraction.
• Kyoto Encyclopedia (KEGG): Pathway mapping to understand drug-induced biological responses.

AI-Driven Workflow

1. Data Ingestion & Curation: Comprehensive collection and standardization of biological data from multiple sources.
2. Target-Based & Reverse Docking: Advanced computational screening using AI-powered docking algorithms.
3. Post-Docking Refinement: Sophisticated analysis and optimization of docking results.
4. FEP Simulations: Precise binding affinity predictions using molecular dynamics for atomic-level accuracy.
5. Report Generation: Detailed analysis and visualization of results for actionable insights.

Key Therapeutic Applications

• Oncology: Identifies new drug candidates for tumor suppression and immune modulation, supporting target identification, pathway analysis, and biomarker discovery.
• Infectious Diseases: Discovers antiviral and antibacterial applications for known compounds, including broad-spectrum activity assessment, resistance analysis, and combination therapy exploration.
• Rare Diseases: Finds alternative therapies for under-researched conditions, covering novel mechanisms, patient stratification, and orphan indications.
• Neurological Disorders: Repurposes existing drugs for neurodegenerative disease interventions, with focus on blood-brain barrier penetration, target validation, and bioavailability.

MedGraph - Rubie™ is built on Medvolt's broader AI infrastructure, which is reported to cut pre-clinical discovery time by 3x, reduce costs by 15x, and lower failure risk by 25%. The platform leverages proprietary high-throughput datasets and NLP solutions, and is supported by a globally collaborative team working with leading pharma and biotech organizations.