This Small Business Innovation Research (SBIR) Phase I project will investigate the feasibility of knowledge extraction from Support Vector Machine (SVM) non-linear models and their results for drug data sets. SVMs are an accurate method of statistical machine learning for data mining. This project references research papers where SVMs have performed very well at predicting activity and properties for drug-like compounds. The lack of interpretability of non-linear models has prevented successful commercial adoption of SVMs by the chemical and pharmaceutical industry. This project describes experiments to test different methods of non-linear feature discovery using drug data sets in order to determine their potential for commercialization. The broader impacts of this activity for improving knowledge extraction of non-linear SVM models include a broad range of applications from improved oil exploration to credit fraud detection. The company's goal is to provide innovative drug discovery informatics software research tools to the pharmaceutical industry. It will present the innovative capabilities discovered from this grant to industry. The need for improved research tools for drug discovery is great. This is demonstrated by the fact that the top ten largest pharmaceutical companies spent over 33 billion dollars in 2002 on drug discovery research. Improvements to data mining methods will assist pharmaceutical companies in finding new cures to deadly diseases such as HIV. Predictive modeling can be employed to virtually screen, prioritize and then decide which of these compounds will be tested in the lab. This can reduce the number of compounds tested and increase their success rate. By providing innovative methods of knowledge discovery, deeper insight can be gained into why compounds exhibit activity and properties necessary to treat certain diseases, leading to improved derivatives. This insight will help scientists discover better drugs faster
