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Development of AI/ML-Driven Data Science Platform for Accelerated Drug Discovery

verytechnology.com

Medical

Biotechnology

Information technology

Challenges in Drug Discovery Efficiency and Talent Management

The client faced significant delays in drug discovery due to inefficient data-driven decision-making processes and struggled to access and manage external AI/ML engineering talent, hindering platform scalability and innovation.

About the Client

A data science-driven drug discovery firm leveraging AI/ML to accelerate pharmaceutical R&D

Key Goals for Platform Development

Enhance drug candidate ranking capabilities by 62%
Build a scalable end-to-end data science stack
Improve integration of external engineering talent
Optimize data processing and storage on cloud infrastructure
Develop robust machine learning models for biomedical analysis

Core System Capabilities

Data ingestion and ETL pipeline management
Cloud-based data processing with GCP
Machine learning model development and evaluation
Data versioning and pipeline tracking
Collaborative dashboarding and visualization tools
Natural language processing with LLM integration

Technology Stack Requirements

DTL (Data Versioning)

Google Cloud Platform (GCP)

Apache Spark

BigQuery

ChatGPT

Large Language Models (LLM)

System Integration Needs

Existing biomedical data repositories
Third-party API for pharmaceutical databases
Internal collaboration tools for distributed teams

Operational Requirements

High scalability for handling large biomedical datasets
Data security and compliance with healthcare regulations
System reliability for continuous R&D operations
Performance optimization for real-time analytics

Expected Business Outcomes

The platform is expected to reduce drug discovery timelines by 62%, significantly lower R&D costs through optimized resource allocation, and enable faster market entry for critical pharmaceutical products while improving collaboration efficiency with external engineering talent.