Predicting new chemistry
In chemical synthesis planning applications, the goal is to generate accurate and diverse sets of synthetic routes. However, data-driven computational applications can only be as good as the underpinning data.
Read this FREE white paper to discover how collaboration between CAS and Bayer demonstrated the significant impact quality data can have on the predictive power of machine learning models.
About CAS
CAS connects the world’s scientific knowledge to accelerate breakthroughs that improve lives. CAS empower global innovators to efficiently navigate today’s complex data landscape and make confident decisions in each phase of the innovation journey.
As specialists in scientific knowledge management, CAS builds the largest authoritative collection of human-curated scientific data in the world and provides essential information solutions, services, and expertise.
CAS is a division of the American Chemical Society. Connect with CAS at cas.org