Resources

A collection of feature articles, case studies, and whitepapers from CAS. News and trends spanning scientific research and information technology.

A collection of feature articles, case studies, and whitepapers from CAS. News and trends spanning scientific research and information technology.

Resources Index

Drug delivery options image for CAS whitepaper
White papers

Emerging Trends in Drug Delivery

Emerging Trends in Drug Delivery is a CAS whitepaper which highlights four technologies that have shown particular growth in recent years and are expected to be key innovation areas in the future.

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New findings reveal innovation in small molecule discovery is accelerating

Though an increasing number of novel chemical compounds are being synthesized each year, there is growing concern that innovation may be stagnating in small molecule discovery. However, new research published by CAS scientists in the October 2019 Journal of Organic Chemistry reveals that the pace of small molecule innovation is actually accelerating from a structural perspective and offers insights into finding fruitful new areas for further investigation as chemists navigate a vast and largely unexplored chemical space.

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Data modeling: A fundamental pillar of your future AI technology

How your data is organized and stored, and the data relationships within, is defined by a data model. An effective model allows users across your organization to easily understand how the business operates. It is the linchpin of almost every high-value business solution, with its greatest value realized when applied beyond the boundaries of individual lines of businesses (LOBs) or operations within an organization. This data model is a strategic pillar for information management, upon which the success of future business-critical projects depend.

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Accelerating innovation in materials science with machine learning

Given the wide range of applications and benefits offered by ML, there is a current push to implement it in the materials science sector, with many R&D-based organizations investing heavily in the development of digital strategies. However, one challenge these teams face is that scientific data is often complex and disconnected. This is a problem because ML systems rely on well-organized, high-quality data. So, how can you effectively apply ML to accelerate innovation and growth in your materials science company?

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