AI-Enabled Partnership Intelligence Platform for Alzheimer’s Research
In healthcare and pharmaceutical R&D, identifying the right partnership at the right time can define the success of an entire therapeutic program. This case study shows how Langate helped a global neurology-focused pharma company improve its approach to licensing strategy in Alzheimer’s research by combining scientific data with AI-driven analysis.
Summary
Langate implemented an AI-powered platform that unified biomedical data and market intelligence into a structured partnership evaluation workflow.
Quick Facts
- Timeline: 6 months
- Team: 6 specialists
- Output: 30 validated Alzheimer’s targets and assets
- 70% faster evaluation of opportunities
- Enabled multiple high-value collaboration reviews
Project Scope
The project covered data integration, AI model configuration, and deployment of analytics dashboards for business and research teams. The focus was on delivering a practical, usable system within a regulated pharma environment.
Tech Stack
- Python
- Vertex AI
- Spotfire
- REST APIs
Data sources: OpenTargets, ChEMBL, Pharmaproject, PubMed, GWAS Catalog
The Client
The client is a global pharmaceutical company specializing in neurology, with a focus on Alzheimer’s disease. They aimed to improve how potential partnerships and licensing opportunities were identified and assessed.
Business Requirements
- Accelerate evaluation of partnership opportunities
- Improve reliability and consistency of decisions
- Combine clinical, scientific, and market perspectives
- Enable collaboration between BD and R&D teams
- Ensure scalability across future therapeutic areas
The Problem
The client’s partnership evaluation process was slow and resource intensive. Analysts manually gathered and compared data from multiple sources, including literature, patents, and drug pipelines. This approach limited speed, reduced visibility into emerging opportunities, and led to inconsistent prioritization.
The Challenges
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Working with complex and heterogeneous biomedical datasets - Ensuring relevance of insights for both scientific and commercial stakeholders
- Integrating new capabilities into existing workflows
- Maintaining data quality and governance
Problems Revealed
The project revealed that scientific and business evaluation processes operated in silos. While data was available, it was not structured in a way that supported fast, cross-functional decision-making.
The Solution
Achievements
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30 high-confidence Alzheimer’s targets and assets identified
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70% reduction in evaluation time
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Faster and more informed partnership decisions
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Improved collaboration between BD and R&D
The platform enabled a shift from manual research to structured, data-driven partnership strategy.
Future Plans
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The client plans to extend the platform to additional therapeutic areas and integrate it with broader R&D decision-making processes.
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Langate continues to support the evolution of the system toward a fully connected, AI-assisted strategy layer.
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