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CDD for Neglected Disease Research

Key benefits for neglected disease researchers

  • Join existing communities organized around therapeutic or target areas, or start a new one.
  • Augment your research data with extensive research data curated by CDD from the scientific literature as well as data shared openly and directly from the laboratories of leading scientists.
  • Exploit state-of-the-art database software and support at an affordable price.
  • Build highly-networked virtual drug discovery and development communities.
  • Bridge the complementary disciplines of biology, chemistry, computational chemistry, and medicinal chemistry to advance new leads more effectively.
  • Maintain research continuity as the research groups participating in a collaborative project change.
  • Ensure continued access to the results of joint research activities by archiving all research data in a central, industrial-strength database.
  • Enable new research paradigms that maximize the benefits of highly-interactive collaborations.
  • Easily set-up and manage collaborations involving multiple research groups located anywhere in the world.
  • Avoid the need to purchase and maintain hardware and software, avoid the need to pay an IT specialist to support users, and avoid the need to worry about the security and integrity of research data.
  • Protect the commercial value inherent in the research data you generate, so promising approaches can be patented and commercialized, while also encouraging colleagues to share data to the greatest extent practical.

Key benefits for all users

  • Capture and organize fragmented data.
  • Keep pace with high-throughput experiments so data sets don’t pile up unanalyzed.
  • Avoid performing redundant research work.
  • Maintain project continuity when group members leave.
  • Ensure data integrity and availability by archiving to CDD’s safe and secure repository.
  • Exploit sophisticated cheminformatics and bioinformatics tools without the need for specialized informatics training.
  • Extract the greatest value from preclinical data.
  • Mine your own data together with open-access data through a single, intuitive interface.
  • Integrate dispersed efforts: test Group A's compounds with Group B's models against Group C's new target.
  • Control your data: keep your data completely private, exchange some data confidentially with colleagues you specify, or share openly with the scientific community; specify which data sets to share, with whom to share them, and when they can be shared.