Clarivate Analytics plc, a global leader in providing trusted insights and analytics to accelerate the pace of innovation, has launched an expansion to its Cortellis suite of life science intelligence solutions with programmatic access to scientific data typically used in drug discovery and early stage development. The initial launch includes continually-updated systems biology datasets, which enable researchers to analyse molecular pathways and advance discovery research.
With R&D and commercialisation costs exceeding $2 billion to bring just one asset to market and average sales per asset declining by 50 percent since 2010, companies are increasingly challenged to improve pipeline productivity. This means they require more efficient and cost-effective methods to access, extract and analyse large, complex datasets – as well as enhance their visibility to potentially-viable novel therapies. Conducting this research necessitates highly targeted searches using discrete information sets not supported by traditional database licensing models.
Leveraging the Cortellis Content-as-a-Service (CaaS) model, researchers can now access industry-recognised statistical packages to efficiently build scripts and automate workflows that incorporate vast scientific and discovery intelligence, which includes more than 2 million molecular interactions, 1.5 million molecular pathways, 270,000 gene variants and 2.5 million publication references. This will help researchers to quickly test new concepts, hypotheses and analytic tools, ultimately fostering new use cases and greater pipeline productivity. The service is powered by Cortellis Cloud, an integrated, scalable technology platform that serves as a single point of access to Cortellis content.
Cortellis CaaS enables direct access to industry-leading content to Clarivate customers and partners, and facilitates interoperability with partner and other third-party applications via APIs. Programmatic access to Cortellis data will continue to expand in the coming months, adding APIs that encompass both discovery and pre-clinical research; python libraries that apply machine learning code; integration with third party open-source content exchanges; and new services that deliver content under a usage-based model.
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