The increasing resistance of several phytopathogenic fungal species to the existing agrochemical fungicides has alarmed to the worldwide scientific community. There is no available methodology to predict in an efficient way if a new fungicide will have resistance risk due to fungal species which cause considerable crop losses. In an attempt to overcome this problem, a multi-resistance risk QSAR model, based on substructural descriptors was developed from a heterogeneous database of compounds. The purpose of this model is the classification, design, and prediction of agrochemical fungicides according to resistance risk categories. The QSAR model classified correctly 85.11% of the fungicides and the 85.07% of the inactive compounds in the training series, for an accuracy of 85.08%. In the prediction series, the percentages of correct classification were 85.71 and 86.55% for fungicides and inactive compounds, respectively, with an accuracy of 86.39%. Some fragments were extracted and their quantitative contributions to the fungicidal activity were calculated taking into consideration the different resistance risk categories for agrochemical fungicides. In the same way, some fragments present in molecules with fungicidal activity and with negative contributions were analyzed like structural alerts responsible of resistance risk.