LLM-Powered Drug Discovery
The Challenge
Drug discovery is a 10-15 year, $2.6 billion process with a 90% failure rate in clinical trials. Pharmaceutical companies need to accelerate timelines while reducing costs.
Root Cause Analysis
- Vast search space: Billions of possible molecular combinations to evaluate
- Literature overload: Thousands of papers published daily, impossible for humans to process
- Trial inefficiency: Poorly designed trials with wrong patient populations
- Data silos: Preclinical, clinical, and real-world data in separate systems
How We Solve This with Cloud Technologies
AI-Powered Drug Discovery Platform
- Molecular generation: LLMs trained on ChEMBL/PubChem generate novel candidates with desired ADMET properties
- Literature mining: NLP pipelines (GPT-4, BioBERT) extract drug targets, interactions, and side effects from 30M+ papers
- Clinical trial design: ML models identify optimal endpoints, patient populations, and trial sites
- HIPAA-compliant infrastructure: All data processed in SOC2/HIPAA-certified cloud environments
Architecture
- Data lake: Patient data, genomics, clinical results in a secure, encrypted lakehouse
- ML platform: SageMaker/Vertex AI for model training with GPU clusters
- Knowledge graph: Neo4j for drug-target-disease relationship mapping
- Compliance layer: Encryption at rest/transit, audit logging, consent management
Business Impact
- 50% reduction in lead identification time
- 30% improvement in clinical trial success rates
- Full compliance with FDA, EMA, and HIPAA requirements