Abstract: The novel TOMOCOMD–CARDD approach has been introduced here for the classification and design of antifungal agents using computer-aided molecular design. For this purpose, no stochastic and stochastic atom-based quadratic fingerprinting were used to codify the antifungal-related chemical structure information from a comprehensive data set of 2478 organic compounds having a great structural variability, 1087 of them being antifungal agents covering the broadest antifungal mechanisms of action known so far. The two ligand-based antifungal-activity classification models obtained by using Linear Discriminant Analysis, including no stochastic and stochastic indices, classified correctly 90.73% and 92.47%, respectively, of 1772 chemicals in the training set. These models showed moderate-to-high Matthews correlation coefficients (MCC of 0.81 and 0.85) as well as a very good accuracy, sensitivity, specificity and false alarm rate. These models were able of classifying correctly 92.16% and 87.56% of 706 compounds in an external test set. In general, the TOMOCOMD–CARDD models were best in predicting antifungal activity when compared with six of the most recent models reported so far; indicating that this approach could be very useful to identify (design and/or select) new antifungal agents against life-threatening fungal infections.