Collaborative research in drug discovery is a dynamic process. As findings are made, discoveries need to be revised and new data integrated and analyzed.
When tools, teams, and data are all siloed, collaboration is blocked. The harder it is to collaborate, the more it feels like everyone has less and less time for valuable, individual work.
This short e-guide by the Life Sciences team at KNIME presents case studies of how drug discovery companies are using KNIME to access and integrate data of any type or source, establish powerful and reusable ETL processes for data curation, expand advanced analytics to non-AI experts, and link up coders and non-coders.
Discover how Nuvisan, CENTOGENE, SciLifeLab, and Wave Life Sciences are enabling multisite and multi-disciplinary teams to collaborate with agility and accelerate drug discovery.
AI/ML techniques have the potential to improve drug success rates and lower costs by as much as 70%, yet adoption is low. How can more people benefit? Enable multi-disciplinary teams to leverage modern ML techniques with valuable domain expertise through guided analytics.
Maintaining nimble systems that expose the data to multi-disciplinary teams while enabling a variety of needs and users is a huge challenge for large organizations. Enable scientists to use their domain-specific tools and programming languages within a consistent environment, with extensions for RDKit, ImageJ2, SeqAn, and integrations for Python, R, and more.
Collaboration played a pivotal role in the development of the COVID-19 vaccines and therapies: Sharing ideas allows scientists to solve problems faster. Connect distributed teams with the no-code/low-code approach and enable access to advanced analytics for everyone.