This interdisciplinary research project is jointly funded in three NSF directorates: CISE/IIS, BIO/DBI and ENG/BES. The techniques developed in this research result in a new framework for the virtual discovery of new pharmaceuticals or materials. The basic idea is to utilize large existing pharmaceutical databases as input for a new type of structure/activity correlation methodology in order to calculate a large set of new and traditional descriptors to create improved Quantitative Structure-Activity Relationship (QSAR) models that characterize and predict important biological responses.
      Once the descriptors have been determined and a predictive model has been built, thousands of new potential molecules, chemically similar to those of the benchmark data set, are scanned from large databases and are evaluated for their chemical properties based on the predictive model. The aim is to target a few novel molecules with potentially attractive pharmaceutical properties that can then be tested further in the traditional way in the laboratory. Computationally intelligent data mining techniques are vital to extract the information necessary to select these novel molecules. This research applied novel machine learning paradigms such as semi-supervised learning with capacity control. These algorithms predict desired biological responses and generate QSAR models using both known (labeled) and unknown (unlabeled) biological responses. This project involves the development of an infrastructure of computationally intelligent computer codes that allow for the virtual design of novel pharmaceuticals or the improvement of existing pharmaceuticals. The proposed methodology is applicable to most pharmaceuticals for which a database of responses is available. The ultimate pay-off of this methodology is expected to lead to the rapid invention of new drugs for new or known society threatening diseases where a very fast response is warranted.