OpenLB
Open Library of Bioscience

Classification and QSAR analysis was performed on a large set of hydantoin derivatives with measured anticonvulsant activity in mice and rats. The classification set comprised 287 hydantoins having maximal electroshock (MES) activity expressed in qualitative form. A subset of 94 hydantoins with MES ED(50) values was used for QSAR analysis. Numerical descriptors were generated to encode topological, geometric/structural, electronic, and thermodynamic properties of molecules. Analyses were performed with training and test sets of diverse compounds selected using their representation in a principal component space. Cell- and distance metric-based selection methods were employed in this process. For QSAR, a genetic algorithm (GA) was used for selecting subsets of 5-9 descriptors that minimize the rms error on the training sets. The most predictive models have rms errors of 0.86 (r(2) = 0.64) and 0.73 (r(2) = 0.75) ln(1/ED(50)) units on the cell- and distance metric-derived test sets, respectively, and showed convergence in the selected descriptors. Classification models were developed using recursive partitioning (RP) and spline-fitting with a GA (SFGA), a novel method we have implemented. The most predictive RP and SFGA models have classification rates of 75% and 80% on the test sets; both methods produced models with similar discriminating features. For QSAR and classification, consensus schemes gave improved predictive accuracy.