Latest Trends in Science & Intellectual Property

Resources Index

Improved Data Integrity Enhances R&D Efficiency

Case Studies

How CAS Repaired Incorrect Internal Data to Accelerate R&D Growth and Reduce Annual Expenses

Data silos compromise the value of digital assets and decrease efficiency. See how one company projected an annual savings of over $800,000 through harmonization and standardization of internal data.

Case Studies

How a Partnership Between Brazil Researchers and CAS Preserved Biodiversity Data

In this case study, learn how CAS collaborated with natural product researchers in Brazil to build an accessible data collection, representing over 54,000 substances from Brazilian biodiversity.

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.

Case Studies

ICL Discovers New Uses for Waste Compounds with Better Data Connections

Repurposing chemicals requires first uncovering the right insights from vast amounts of research. This case study shows how CAS enabled the efficient discovery of application leads for 75% of ICL’s target byproduct compounds.

Curated Training Sets for AI-ML thumbnail image

Case Studies

How Curated Training Sets from CAS Improved the Prediction Accuracy and Transferability of an AI Model

Utilizing only internal data can lead to the lack of specific electronic, structure and device properties required to generate innovative machine learning predictions. Learn how one company turned to custom-curated data to expand a limited view of the scientific landscape.

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.

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.

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?

To optimize machine learning predictions, it is best to keep a chemist in the loop

Read how machine learning is being used to identify, screen and prioritize candidate compounds. CAS demonstrates how a machine learning approach will increase accuracy and reliability of predictions.