Thus, screening expenses can be greatly reduced by assaying only the highest scoring compounds, or positive results can be obtained more quickly by assaying those compounds first. These types of models have proven to be extremely valuable for the prioritization of libraries and have already resulted in considerable savings in the purchasing of new compounds. Building the model and performing the evaluation steps can be done quickly. Conventional technologies lack the scalability to address problems of this magnitude with accuracy and speed. Data pipelining applied to other areas Data pipelining can be used in a broad variety of informatics applications beyond predictive modeling.
Standard protocols are available for cleaning, evaluating, and comparing compound libraries.
Chemical databases can be mined for candidate leads, while simultaneously applying outside criteria from text collections, such as Medline. In addition, assay data can be processed and condensed before archiving in a relational database system. By streaming data through automated processing protocols, new scientific approaches become possible, and discovery is ultimately accelerated. Conclusions Data pipelining has emerged as a practical technology for accelerating the discovery process. Research at Agouron with Pipeline Pilot has demonstrated the value of data pipelining as a tool in drug discovery research and an aid for managing large data collections in a flexible manner.