Stop Re-Inventing the Wheel: How Semantic Content Libraries Accelerate Drug Repurposing
The Value of “Old” Drugs
Discovering a new chemical entity (NCE) is risky 90% fail. Drug Repurposing (finding new uses for existing drugs) is the strategic shortcut. These drugs have already passed toxicity screens; their safety profiles are known.
A famous example of this is Baricitinib. Originally a rheumatoid arthritis drug, it was identified by AI algorithms as a potential treatment for COVID-19 because it disrupted the viral entry mechanism. This wasn’t luck; it was the result of mining vast Knowledge Graphs to find hidden connections between the drug’s mechanism and the virus’s pathway.
How Solix Automates Serendipity
The signal for the next blockbuster repurposing candidate is likely already sitting in the public domain. It is buried in the millions of unstructured documents published every year patents, academic journals, and clinical trial reports.
No human team can read them all. But Solix EAI Pharma can.
1. LLMs for Insights
Solix uses Large Language Models (LLMs) to ingest vast repositories of literature. It extracts entities (Drugs, Genes, Diseases) and, crucially, the relationships between them. This creates a Semantic Content Library a structured map of scientific knowledge.
2. Connect the Dots (Link Prediction)
Once the data is structured, Solix applies Link Prediction algorithms. It looks for “missing edges” in the graph. For example, if Drug A targets Gene B, and Gene B is a known driver of Disease C, the system infers that Drug A might treat Disease C, even if no paper has ever explicitly stated it.
3. From Text to Candidates
Solix transforms unstructured text into a prioritized list of repurposable candidates instantly.
- Target-Agnostic Repurposing: Find new diseases for your drug without even knowing the full mechanism.
- On-Target Repurposing: Match your drug’s known target to new disease pathways.
