Using artificial neural networks to classify the activity of capsaicin and its analogues

Back-propagation artificial neural networks (ANNs) were trained with parameters derived from different molecular structure representation methods, including topological indices, molecular connectivity, and novel physicochemical descriptors to model the structure--activity relationship of a large series of capsaicin analogues. The ANN QSAR model produced a high level of correlation between the experimental and predicted data. After optimization, using cross-validation and selective pruning techniques, the ANNs predicted the EC50 values of 101 capsaicin analogues, correctly classifying 34 of 41 inactive compounds and 58 of 60 active compounds. These results demonstrate the capability of ANNs for predicting the biological activity of drugs, when trained on an optimal set of input parameters derived from a combination of different molecular structure representations.