D948 Nucleic Acids Research, 2012, Vol. 40, Database issue For example, it would be extremely valuable to enable obtaining integrated information about expression, muta- tion and functional annotation for a target together with its druggability, identify suitable cellular models for drug testing, identify chemical tool compounds and understand the wider context of the likely action of these compounds on the target’s pathway neighbours, all within one place, and without extensive expertise in computational tech- niques, medicinal chemistry and genomics. Such integration requires smart underlying technical data modelling, data cross-referencing and redundancy elimination, as well as regular and reliable update pro- cesses. canSAR addresses these needs by integrating data from a plethora of domains and providing a use-case driven web-based interface to help answer translational research questions. Database design and processing pipeline The design and implementation of canSAR has been entirely use-case driven. canSAR was designed in a modular manner such that the modelling and inclusion of new data types in the future [e.g. clinical outcome or drug metabolism and pharmacokinetics (DMPK) data] is rela- tively easy. This provides the ability and support to expand the functionality and usefulness of canSAR as new requirements are found. The data model is designed around elemental and association data types. Elemental types (e.g. molecular target, compound, cell) represent the core components of the system. These elements can then be associated via different association types, e.g. a compound and a molecular target can be associated via a ‘screening’ association, a ‘drug action’ association, or a ‘3D-structural complex’ association. The associations in turn are then linked to the experimental data, sources of origin and publication references where relevant. These generic key data types provide the ﬂexibility described above. Figure 1 shows an illustration of this modular design with the core elemental data types represented as large jigsaw pieces and the association data as the inter- connecting pieces. Using this paradigm, new associations Figure 1. Modular design of canSAR: large pieces represent elemental types, small pieces represent associations. This design allows new elemental data types to be added and new interconnecting (association) data to be introduced.