Targeted Task Models for Cheminformatics Process Development
Co-Investigator: Kristin P. Bennett
Professor, Department of Mathematical Sciences, Rensselaer Polytechnic Institute
Support Vector Machines (SVM) and Partial Least Squares (SVM) will be customized to target the goals of a given cheminformatics tasks leading to enhanced performance. While we illustrate this process using the Bioseparations Module, the general approach may be applied to any of the applications discussed in this proposal. This grant is essential for such targeted approaches because they require the close collaboration of the chemistry and learning experts and the development of flexible learning frameworks that can be easily customizable to the target problem. As discussed in the Bioseparations Module
, development of a separation methodologies currently requires extensive experimental investigation of the operating variables, e.g. stationary phase material, salt type, pH, gradient conditions and/or displacers material. Kernel PLS and SVM QSPR models have shown that inference models can support discovery and understanding of bioseparations (Breneman et al 2003). By developing extensions of these approaches targeted towards ranking and multi-task modeling, we can further accelerate the discovery process.