Application of data mining to quantitative structure-activity relationship using rough set theory

Rough set theory (RST) is a new data mining method originally proposed in chemometrics. RST selects the least descriptor sets for discriminating one sample from the others. These descriptor sets are called reducts. RST constructs any possible rules for high activity using the specific reduct. We have used dihydrofolate reductase (DHFR) inhibitors as a validation set of RST. This data set has been thoroughly investigated in several studies and the structural requirements for high activity have been well known. The RST-based rules were well matched to these structural requirements and thus utility of RST has been proved. According to the success in this study, further applications to data sets that have more diverse compounds and more noisy activity would be expected.