Fuzzy Models for Quantitative Structure-Activity Relationship (QSAR)
An important issue in QSAR modeling is of robustness, i.e., model should not undergo overtraining and model performance should be least sensitive to the modeling errors associated with the chosen descriptors and structure of the model. The method studied in this project established a robust input-output mappings for QSAR studies based on fuzzy ``if-then'' rules. The identification of these mappings (i.e. the construction of fuzzy rules) is based on a robust criterion being referred to as ``energy-gain bounding approach''. The method minimizes the maximum possible value of energy-gain from modeling errors to the identification errors. The maximum value of energy-gain (that will be minimized) is calculated over all possible finite disturbances without making any statistical assumptions about the nature of signals. A comparison of the method with Bayesian regularized neural networks was provided through the QSAR modeling examples of 1) carboquinones data set, 2) benzodiazepine data set, and 3) predicting the rate constant for hydroxyl radical tropospheric degradation of 460 heterogeneous organic compounds.