A key challenge in MD simulations lies in the accurate parameterization of force fields, which define the (non-)bonded interactions between atoms. Classical MD simulations are limited in their ability to model chemical reactions, such as bond breaking or formation. However, recent innovations, including constant-pH MD techniques, now enable the simulation of dynamic (de)protonation events of proteins and lipids under varying pH conditions . MD simulations are computationally demanding, often requiring access to high-performance computing (HPC) facilities. To mitigate these demands, researchers frequently employ coarse-grained force fields, such as MARTINI and SIRAH , which simplify systems by grouping atoms into larger "super-beads". This approach reduces the degrees of freedom and computational complexity, while still capturing essential system dynamics. Atomistic force fields like CHARMM and AMBER remain indispensable for highly detailed simulations, albeit with higher computational costs. Machine learning: Machine learning (ML) has become a powerful bioinformatics tool for classifying, predicting, and designing novel AMPs. By identifying patterns in training data, ML methods generate new peptide sequences with potential antimicrobial activity. Common techniques include support vector machines (SVMs), neural networks (NNs), discriminant analysis (DA), random forests (RF), hidden Markov models (HMMs), and long short-term memory (LSTM) models. These methods vary in their dataset requirements, interpretability, and ability to generate novel sequences. Simpler models, such as RF, DA, SVM, and HMMs, perform well with small to medium-sized datasets, while more complex approaches like NNs and LSTMs require larger datasets and longer training times to avoid overfitting and limited sequence novelty. A critical challenge across all ML approaches is the scarcity of standardized, high-quality experimental data from diverse sources. AMP datasets are often small, which constrains the capabilities of advanced models. Furthermore, the lack of pharmacokinetic (PK) and toxicological data limits the clinical translation of in vitro active peptides. Despite these limitations, ML methods excel in rapidly identifying active candidates, as demonstrated in recent studies . However, designing AMPs that overcome inherent challenges, such as poor PK profiles, remains a significant hurdle, highlighting the need for more robust datasets and advanced modeling strategies. Fluorescence microscopy: Imaging of specific bacterial structures through fluorescence labeling. The huge advantage is the applicability to both fixed and live material. Spatial resolution of conventional approaches such as wide-field, confocal, two-photon or TIRF and lightsheet microscopy is down to 200 nm, i.e. on the scale of sub-cellular bacterial structures, and down to the molecular level (individual proteins and molecular aggregates) through super-resolution microscopy approaches such as STED, STORM/PALM, SIM or MINFLUX microscopy, and dynamics can be recorded with milli-second to second time resolution, depending on the method in hand . Advantages are the molecular specificity and possibility to observe both fixed and live bacteria. Disadvantages are the need for fluorescence staining, increasing sample preparation complexity, and the potential phototoxicity on the sample as well as photobleaching of the labels, limiting recording times. Electron microscopy (EM) and X-Ray or XUV microscopy: Imaging of bacterial structures using electron beams (EM) or X-ray and XUV radiation. The big advantage of these microscopy techniques is the high spatial resolution, being able to resolve sub-cellular features down to the molecular level and even highlight structural details of (macro)molecules . The disadvantage are the need for complex instrumentation performing in vacuum, accurate sample fixation and preparation (such as cryo-fixation), making live-cell measurements impossible, and intense data analysis. Raman microscopy: Label-free molecular fingerprints of a bacterial sample. Raman spectra allow to identify certain molecular patterns of a sample like inflammation or infection states of samples or differences in molecular compositions or status of bacteria . The big advantage of Raman microscopy is its label-free use, i.e. it can be employed directly to a sample of interest such as a patient sample or bacterial colony. The disadvantage is a lower spatial and temporal resolution than other microscopy techniques (which is still on the sub-bacterial scale) and a rather low signal yield with low signal-to-noise levels, which is tackled with non-linear and multimodal Raman approaches . Atomic force microscopy (AFM): Scanning of a sample surface allows the characterization of its properties down to the molecular level, such as surface height profile, stiffness or molecular interaction forces . The advantage of AFM is its high spatial resolution and high information content, while disadvantages involve limitation to surfaces only and the use of tips that could potentially influence the sample.