To demonstrate the usefulness of the constructed models for understanding multi-component membrane properties, we predicted later packing (ares per lipids) and membrane thicknesses of common biological membranes based on their lipid compositions reported in the literature, see table . The model predicts substantial 50% difference in area per lipid between most densely (influenza virus) and loosely (mitochondria) packed membranes. Difference of 0.8 nm in thickness is predicted between thinnest (bacterial) and thickest (plasma) membranes. Such differences are expected to effect on many biologically relevant functions of membranes, such as permeation, cholesterol flip-flops (see next sections), and interactions with proteins , demonstrating that NMRlipids databank can be used to give valuable insights on biologically relevant properties of complex biological membranes. Most importantly, the delivered programmatic access to increasing amount of MD simulation data enables training of machine learning models that predict various membrane properties for all. Lipid flip-flops from one bilayer leaflet to another play an important role in lipid trafficking and regulating membrane properties . Phospholipid flip-flop events are rare when not facilitated by proteins, occurring spontaneously on the timescale of hours or days, while cholesterol, diacylglycerol, and ceramide flip-flop much more often. Still, the reported timescales range from minutes to sub-millisecods . These timescales were previously accessible only by coarse-grained simulations or free energy calculations , and atomistic simulations reporting cholesterol flip-flop events have been published only recently .