Pharma Company Accelerates Lead Identification by 50% with AI-Driven Discovery
The Challenge
A mid-sized pharmaceutical company needed to accelerate early-stage drug discovery to remain competitive. Traditional screening and literature-based hypothesis generation were slow and costly; lead identification cycles averaged 18–24 months. Scientific teams were overwhelmed by data volume from genomics, assays, and publications.
Our Approach
We built an AI-powered discovery platform that integrates internal assay data, public biomedical knowledge graphs, and literature. Machine learning models predict compound–target interactions, prioritize candidates, and generate explainable hypotheses. The system supports collaboration across medicinal chemistry, biology, and computational teams.
Integrated internal assay, genomics, and chemistry data with public knowledge graphs and literature in a unified discovery data layer
Developed ML models for compound–target interaction prediction and lead prioritization with explainability for scientific review
Built hypothesis-generation workflows that combine model outputs with domain rules and literature evidence
Designed collaborative workspaces for medicinal chemistry, biology, and computational teams with shared experiments and audit trails
Implemented governance for data access, model versioning, and documentation to support regulatory and IP requirements
Established feedback loops so new experimental results continuously improve model performance
Key Features
Unified discovery data layer (internal and external sources)
Compound–target prediction and lead prioritization
Explainable hypotheses and evidence linking
Collaborative workspaces and experiment tracking
Governance and auditability for compliance and IP
Impact & Results
Shortened lead identification cycles by 50%, from 18–24 months to 9–12 months
Increased throughput of screened hypotheses by 3x while improving hit rates by 25%
Reduced cost per successful lead by an estimated 40% through better prioritization
Enabled cross-functional visibility and audit trails for regulatory and IP documentation
Technologies & Capabilities
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