Industry News: CAS and ACD/Labs collaborate to provide predictive insights to scientists

ACD/Labs, an informatics company that develops and commercializes software in support of R&D, has announced a new multi-year collaboration with CAS, a division of the American Chemical Society, to aggregate additional predictive property data from ACD/Labs predictive tools into the CAS Content Collection™.

>CAS and ACD/Labs are long-time collaborators serving scientists in R&D. More than twenty years ago, CAS and ACD/Labs announced their first collaboration, introducing industry-standard physicochemical and molecular property data to the CAS Content Collection, delivered to scientists through CAS solutions including STN^® and SciFinder^®.  In 2008, predicted NMR data from multiple nuclei (1H, 13C, 15N, 19F, 31P), used widely in research for structure elucidation and verification, was added.

This new collaboration enables CAS to significantly expand the searchable reference data that will now be available to scientists through CAS solutions, including the CAS SciFinder Discovery Platform and the STN IP Protection Suite. The most recent version of NMR prediction algorithms delivers larger experimental training sets and increased prediction accuracy and reliability, ensuring scientists are using the highest quality data available to make their decisions. CAS is also enhancing its physicochemical property prediction algorithms—for logD, logP, pKa, aqueous solubility, and more—to ACD/Labs most recent Percepta release with expanded training sets. In addition to these fundamental properties, CAS is expanding ADMET information in its offerings. Used widely across pharmaceutical, environmental, and other areas of research, the full range of physicochemical, ADME, and toxicity data will be invaluable to researchers. CAS’s investment in the newest predictive technologies from ACD/Labs will also ensure enhanced performance across their extensive libraries.

“At ACD/Labs we share CAS’s vision of facilitating innovation by providing scientists with high quality data. We’re continually expanding the training sets of our predictive algorithms to provide reliable predictions to support scientists from initial scientific conceptualization ‘what should I make next?’, through each iterative round of the design-make-test-analyze cycle”, said Andrew Anderson, VP Innovation & Informatics Strategy, ACD/Labs. “This recent upgrade further solidifies a continuity for more productive and innovative scientific experimentation.”

“Pinpointing insights that inspire scientists to accelerate discoveries is at the heart of our strategy to continuously improve the sophistication of our solutions and expand our reach across increasingly interconnected scientific domains,” said Dr. Gilles Georges, CAS Chief Scientific Officer and Vice President for Content Operations. “High-quality, accurate scientific information is the foundation on which our customers accelerate innovation.”

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