Cloud computing for fast prediction of chemical activity

  1. Lookup NU author(s)
  2. Dr Jacek Cala
  3. Dr Hugo Hiden
  4. Professor Paul Watson
  5. Dr Simon Woodman
Author(s)Cala J, Hiden H, Watson P, Woodman S
Publication type Conference Proceedings (inc. Abstract)
Conference Name2nd International Workshop on Cloud Computing and Scientific Applications (CCSA)
Conference LocationOttawa, Canada
Year of Conference2012
Source Publication Date13-16 May 2012
Full text for this publication is not currently held within this repository. Alternative links are provided below where available.
Quantitative Structure-Activity Relationships (QSAR) is a method to create models that can predict certain properties of compounds. Because of the importance of QSAR in designing new drugs, ability to accelerate this process becomes crucial. One way to achieve that is to be able to quickly explore the QSAR model space in the search for the best models. The cloud computing paradigm very well fits such a scenario, thus we designed and implemented a tool for exploration of the model space using our e-Science Central platform supported by the cloud. We report on scalability achieved and experiences gained when designing this system. The acceleration obtained is much beyond what existing QSAR solutions can offer, which opens potential for new interesting research in this area.