The world’s most comprehensive human-curated scientific data collection
The CAS Content Collection™ is expertly curated to transform 150+ years of global research into harmonized, AI-ready knowlege that powers faster, more confident decisions for chemistry, life sciences, materials, and beyond.
Trusted by the world's leading Innovators
Explore CAS data
Explore the comprehensive data types available from CAS, including chemical substances and reactions, bioactivity, clinical trials, patent intelligence, and more.
Your research teams want to be advancing science, not preparing data.
Partner with CAS to eliminate data bottlenecks hindering your success.
Connecting the world's scientific knowledge
The CAS Content Collection is the world's largest repository of human-curated scientific data. Whether you are searching for a CAS Registry Number®, planning a synthesis, conducting prior art research, or training AI models, you are leveraging a century of expert curation and scientific rigor.
Human expertise at scale
Hundreds of Ph.D. scientists curate the CAS Content Collection
Comprehensive coverage
150 years of knowledge across chemistry, life sciences, materials, and engineering
Structured for discovery
Harmonized, interconnected data that delivers human insight and AI impact
CAS data in action
CAS data powers the platforms scientists, IP professionals, and innovators rely on to make confident decisions, from comprehensive research tools to drug discovery platforms.
Explore CAS SolutionsYour AI is only as good as your data
General AI platforms struggle to provide accurate scientific answers and reliable insights because they lack the needed data and context.
Science-smart AI starts with a strong data foundation. Partner with CAS for data and specialized scientific knowledge management expertise to accelerate your projects today and enable an AI roadmap for tomorrow.
What makes caS data AI-ready for science?
Chemical & biological literacy
AI models must interpret IUPAC nomenclature, stereochemistry, biological pathways, and reaction mechanisms correctly to generate reliable results in complex scientific domains.
Structured relationships
Knowledge structures that connect substances, reactions, diseases, targets, and patents across disciplines give models context instead of isolated facts.
Expert validation
Scientists who correct chemical structures, clarify compound relationships, and confirm biological interactions reduce the risk of hidden errors in model training.
Comprehensive domain coverage
Deep training data that spans chemistry, biology, materials science, and pharmacology helps models perform across discovery workflows.
Traceable provenance
Links to peer-reviewed literature, patents, and other authoritative sources with transparent methodology make model outputs easier to audit, reproduce, and trust.