However, the challenges of peptide design might require addressing multiple parameters such as high toxicity, poor oral bioavailability, thermal and pH stability, and functional promiscuity in concert. In addition costs associated with experimental time, human resources, and equipment involved , must also be accounted for. Chemoinformatics, ML, and DL approaches should provide a means for developing safe AMPs with reduced toxicity, predict their antibacterial activity and drug-likeness profile, and accelerate antibiotic discovery . Plisson et al. proposed an ML-guided discovery and design project related to non-hemolytic peptides. The workflow is composed of collecting compounds for an AMP database, computing 56 physicochemical descriptors; developing binary-classifier models to predict hemolytic nature and activity; estimating the domain of applicability, and applying optimized to the discovery of non-hemolytic AMPs from a known database (e.g. APD3) or design novel sequences. The models used in this study include support vector machines, decision trees, random forest, gradient boosting, and k-nearest neighbors. This research is part of a growing series of predictive and generative ML models applied to support the discovery and design of bioactive peptides, including antimicrobial peptides . The authors applied multivariate outlier detection to delineate boundaries of their predictive models (i.e., applicability domain) leading to the identification of outlying sequences . To date, little work is being carried out on estimating the domain(s) of applicability of peptide modeling, although it is necessary for the parallel application of multiple predictors on a given sequence space.