Databricks Accelerates Drug Discovery for Pharmaceutical Leader Through AI-Powered Data Intelligence
AstraZeneca, a global pharmaceutical company, accelerated drug discovery by implementing Databricks Data Intelligence Platform to process millions of data points from thousands of fragmented sources.
Value Results Summary
AstraZeneca discovers, develops, and commercializes groundbreaking drugs to treat serious diseases. However, the company faced a critical bottleneck: scientists lacked the ability to quickly access and leverage all available scientific information as new research emerges. Data was fragmented across internal and external sources, including technical literature, public databases, and proprietary systems. With drug discovery timelines stretching 10-15 years and R&D investments exceeding $5 billion with less than 5% success rates, AstraZeneca recognized the need to accelerate innovation through a data-driven approach. The company struggled with infrastructure complexity, massive volumes of disjointed data spanning hundreds of sources, and the inability to scale Python-based operations to support enterprise-level data science.
AstraZeneca deployed the Databricks Data Intelligence Platform to build a unified foundation for processing scientific data at scale. The platform enabled the company to ingest, parse, and analyze millions of data points across hundreds of sources using natural language processing capabilities. AstraZeneca leveraged Databricks SQL, Delta Lake, and Mosaic AI to construct a knowledge graph of biological insights and facts. This foundation powered a recommendation engine that allows any scientist to generate novel drug target hypotheses for any disease, drawing on all available data. The fully managed platform eliminated infrastructure maintenance burdens while enabling data scientists to build and train machine learning models reliably.
Since implementing Databricks, AstraZeneca has transformed its drug discovery process by removing technical barriers to scale. The platform's cluster management and auto-scaling capabilities improved operational efficiency from data ingest through the entire machine learning lifecycle. Scientists benefit from a shared notebook environment supporting multiple programming languages, accelerating team productivity and collaboration. Most significantly, the recommendation engine has improved AstraZeneca's ability to make informed hypotheses faster, reducing time-to-insight and accelerating time-to-market for novel drugs. The company now reliably extracts meaningful insights from millions of data points across thousands of sources, enabling the development of medicines that help people live healthier lives.
