Advancing drug discovery with Deloitte’s Atlas AI™
Deloitte and Google Cloud share new insights on AI in drug discovery
Discovering a new drug can typically span over a decade and involve significant financial investment. Learn how new artificial intelligence-enabled solutions from Deloitte and Google Cloud can help researchers address redundant experimental efforts and navigate copious data silos to find a faster, more efficient path to breakthroughs.
Data harmonization and connectivity in AI drug discovery
A new perspective on data harmonization, connectivity, and AI models for drug discovery explores how artificial intelligence (AI) and large language models (LLMs)—introduced as Atlas AI in collaboration with Google—can enable new approaches to use AI in drug discovery. Read the report to learn:
- The difference between target-based and phenotypic drug discovery
- Why the most readily exploitable drug targets have already been identified
- How data connectivity and harmonization can help reduce experiments and save time
Shaping the future of AI in drug discovery
The paper also details how organizations can use Atlas AI to make AI drug discovery more efficient over time and reverse the trend of increasing costs and timelines by:
- Reducing time needed to develop new hypotheses for experimental design
- Removing data silos and redundancy of wet lab experiments
- Creating loops across data and scientific workflows
