Value Proposition

Increase confidence:

Hybrid AI validates drug–disease matches using both structured and unstructured evidence.

De-risk development:

Start from compounds with known safety profiles and accelerate to preclinical decisions.

Cut cost and time:

Reduce repurposing cycles from years to months by prioritizing high probability candidates.

How ReviveAI Works

Data Ingestion:

• Curates molecular structures, pathways, phenotypes, clinical trial abstracts and real world evidence.

Dual AI Core:

• Graph Neural Networks: Model relationships across drugs, targets, diseases and pathways.

• Large Language Models: Mine literature and clinical reports to score mechanistic plausibility and safety.

Vector Fusion:

• Converts graph and text insights into dense embeddings for similarity and causality mapping.

Opportunity Discovery:

• Ranks novel drug–disease pairs with predicted efficacy, adverse profile and evidence trails.

Review and Export:

• Interactive workspace for scientists to inspect rationales, export reports and launch validation.

Key Features

• Comprehensive data analysis across structured and unstructured sources

• Synergistic AI integration of GNN, LLM and vector embeddings

• Real world validation framework and evidence traceability

• Scalable architecture that adapts to new data types and indications

50+

3

Years of experience

Happy clients

What You Can Achieve

• Accelerate identification of repurposing opportunities

• Shorten time to market through de-risked candidates

• Enhance precision of drug–disease matching with transparent rationales

Example Outputs

• Ranked candidate list with predicted MoA links

• Safety overlap and contraindication heatmaps

• Study and trial references with extracted rationales

Compliance and Security

Data provenance and auditability by design

Zero Trust data architecture principle

Frequently asked questions

What problem does ReviveAI solve?

ReviveAI accelerates the discovery of new indications for known compounds by scoring drug–disease pairs using graph intelligence and literature-mined evidence, reducing time and cost versus traditional approaches.

What data does ReviveAI use?

Curated chemistry, targets and pathways, phenotypes, trial abstracts, publications, and real-world evidence. The platform maintains provenance and audit trails.

How does the hybrid AI work?

Graph models learn relationships among drugs, targets and diseases, while language models extract mechanistic rationales from text. Embeddings are fused to rank opportunities with confidence scores.

Can we trust the recommendations?

Each suggestion includes evidence links, rationale snippets, pathway alignment and known safety overlaps, supporting human review.

What outputs do we receive?

Ranked candidates, predicted MoA links, contraindication heatmaps, and exportable reports for preclinical planning.

How is data secured?

Zero-trust principles with role-based access, data lineage, and encrypted storage. On-prem or VPC deployment options are available.

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