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Replay coming soon On demand Presented inEnglish Your privacy is important to CAS. More detail about how we use your information is in our privacy policy. Keeping track of patent activities and competitive developments across multiple jurisdictions can be time-consuming and complex. FIZ PatMon automates worldwide tracking of patent families and legal events, enabling users across IP, legal, R&D, and technology management to stay informed without requiring patent-search expertise.

Artificial intelligence has been used to help design biological parts for several years. Proteins, CRISPR-Cas complexes, transposable elements, promoters, and toxin-antitoxin pairs have all been generated by various AI models. However, designing a genome is a different problem, and AI-aided design at that scale has, up until now, been out of reach. Recently, researchers at Stanford University and the Arc Institute report crossing that line.

Join Kathy Van der Herten, CAS Director of Product Management, and Matthew McBride, Director of CAS IP Services℠, to learn how agentic AI fits into existing search workflows, where it delivers the greatest time savings, and which search types benefit most. Matt will walk through a real-world example to show these benefits in action. How agentic AI fits into real search workflows — whether it replaces, adds to, or streamlines existing steps while keeping the IP expert in control.

Expand your synthetic planning! This demonstration covers recent advancements designed to overcome synthetic planning challenges. See how science-smart AI capabilities deliver actionable route predictions and practical synthetic insights through the combined power of CAS SciFinder and CAS Newton. Among the topics covered in this webinar are: When to use CAS Newton as part of your synthesis workflow and how that differs from using the retrosynthesis planner.

The field remains overwhelmingly academia-driven as evidenced by the >97% contribution of journal articles originating from research and academic organizations. The majority of these institutions leading in nanozyme research are located in China (78%), with India, the U.S., and South Korea represented as well (see Figure 2). China also accounts for 39% of corporate-authored journal publications, followed by Italy (8%), the U.S. (8%), and India (7%). Figure 2.

What 368,000 patents reveal about where drug discovery is heading Patents sit at a useful point in the innovation timeline: late enough that a technology is worth protecting, early enough to signal where a field is moving before that direction becomes obvious.

Joint report analyzes nearly one million publications to map emerging trends in polymer science SHANGHAI, CHINA; COLUMBUS, OHIO, U.S.A., August 5, 2026 — PetroChina (Shanghai) Advanced Materials Research Institute Co., Ltd. (SAMRI), a research arm of China National Petroleum Corporation (CNPC) and CAS, a division of the American Chemical Society, today released Polymer Frontiers: A Comprehensive Data-Driven Analysis of Emerging Trends.

Replay coming soon On demand Presented inEnglish August 19, 2026 10:00 AM CET Your privacy is important to CAS. More detail about how we use your information is in our privacy policy. Expand your synthetic planning! This demonstration covers recent advancements designed to overcome synthetic planning challenges. See how science-smart AI capabilities deliver actionable route predictions and practical synthetic insights through the combined power of CAS SciFinder® and CAS Newton℠.

From electronics and healthcare devices to clothing and consumer goods, polymers are some of the most consequential materials ever developed. As the field has grown in scale, it’s also become more complex, with millions of possible sequences to create new polymers and hundreds of billions of dollars in market value.

General AI is just that - general. CAS builds AI on decades of curated scientific data, so your R&D team gets answers it can trust. 67% of R&D leaders are dissatisfied with the speed of AI implementation in their organizations. One of the most common barriers is unreliable or unstructured data. Science-smart AI addresses these risks by integrating high-quality, validated content and technical expertise that reflects the rigor of scientific research.