Logical analysis of data in structure-activity investigation of polymeric gene delivery

To date semi-empirical or surrogate modeling has demonstrated great success in the prediction of the biologically relevant properties of polymeric materials. For the first time, a correlation between the chemical structures of poly(β-amino esters) and their efficiency in transfecting DNA was established using the novel technique of logical analysis of data (LAD). Linear combination and explicit representation models were introduced and compared in the framework of the present study. The most successful regression model yielded satisfactory agreement between the predicted and experimentally measured values of transfection efficiency (Pearson correlation coefficient, 0.77; mean absolute error, 3.83). It was shown that detailed analysis of the rules provided by the LAD algorithm offered practical utility to a polymer chemist in the design of new biomaterials. For the first time semiempirical models for prediction of DNA transfection by poly(β-amino esters), a promising class of biodegradable polymeric carriers, are built using recently introduced extension of logical analysis of data (LAD) methodology, in particular, the LAD regression technique. The employed algorithm demonstrates higher predictive accuracy than that of conventional regression techniques developed on the neural net platforms.