See how enhancing the quality of the input data alone, without changing the algorithm, impacts prediction accuracy of an algorithm designed to assess the biological activity of compounds relative to different targets in this quantitative study by CAS data scientists.
This case study features Konishi Chemical Industry Co. Ltd., a chemical research and development company based in Koizaka, Japan, and how they use SciFinderⁿ to speed up their science to develop profitable solutions in an ever changing market.
A strong data platform enables valuable R&D data to be integrated where and when it is needed. Yet, managing scientific data requires expertise. Researchers at a leading materials organization learned key principles of data management through specialized training at CAS.
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.
