CAS and Novartis Partnered on Chemical Data Platform

The two organizations are collaborating to build a custom discovery platform for internal research data.

Updated on Sept. 29, 2026 in Chemistry

Isometric editorial illustration of stacked laboratory beakers in a modular grid, representing structured chemical research data. — CAS and Novartis Biomedical Research have partnered to integrate proprietary research data into a unified, AI-ready discovery platform to accelerate drug development. AI Illustration. Upload story photo >

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CAS, a division of the American Chemical Society, has partnered with Novartis Biomedical Research to improve the accessibility of internal research data. The collaboration will leverage the CAS SciFinder architecture to standardize experimental reaction records.

Why it matters

By organizing dispersed research data, the initiative aims to enhance the ability of scientists to conduct AI-enabled research. This infrastructure is designed to support modern drug discovery workflows and future computational capabilities.

The initiative draws from the CAS Content Collection, which hosts over 160 million scientist-curated reactions. Data will be processed through the CAS Intelligence Hub to standardize information currently stored across electronic lab notebooks and shared drives.

The players

CAS

This organization is a division of the American Chemical Society based in Columbus that provides scientific information solutions.

Novartis Biomedical Research

This is a research division of the global healthcare company Novartis that focuses on drug discovery and development.

The details

The project utilizes CAS data transformation services to curate and structure proprietary Novartis experimental data into a centralized format. A custom search platform will then be built on top of the established CAS SciFinder architecture to improve workflow efficiency.

September 29, 2026: CAS announced the formal collaboration with Novartis.

The Big Picture

This effort shifts the paradigm for internal research storage by migrating fragmented data into the CAS SciFinder architecture. It moves chemical research discovery away from manual searching in disconnected drives and toward an integrated, AI-ready data environment.

By creating a more searchable repository for experimental reactions, this partnership could accelerate the timeline for developing new pharmaceutical treatments. Standardized data platforms allow researchers to apply large language models and agentic AI to drug discovery workflows more effectively.

The takeaway

Centralizing disparate research data is a critical step for organizations looking to integrate advanced machine learning into their scientific processes. Future drug discovery will increasingly rely on these standardized digital archives to unlock new research insights.