Antimicrobial peptides are a promising class of substances for overcoming multidrug resistant bacteria. In a previous study, high-throughput screening of short peptides for antimicrobial activity against Pseudomonas aeruginosa was performed and the resulting data with 1609 peptides was analyzed using quantitative structure-activity relationship models (QSAR) with excellent prediction power. To avoid non-interpretable black-box behavior of the QSAR models, new features based on fuzzy logic and molecular descriptors were introduced. They were used for comprehensive analysis and visualization. The new features provide good interpretability and are able to differentiate between active and inactive peptides. The statistical relevance of this differentiation was shown using a Wilcoxon rank sum test. The best compromise between activity prediction and interpretability was found for fuzzy terms of the Hopp-Woods scale and the Isoelectric point. A visualization of two of these terms enables an in-depth understanding of regions with active and inactive peptides and the identification of outliers. In addition, we generated rules to explain typical amino acid distributions in active peptides. These rules can be used to increase the probability of finding active peptides in new peptide libraries, which can improve the speed of finding leading substances for drug development against resistant bacteria.