The PerMM and COSMOperm are examples of physics-based calculated methods. PerMM is based on the solubility diffusion model and the positioning of proteins in membranes (PPM) method . PerMM can also calculate the permeability coefficient across four types of membranes (DOPC, BLM, CACO/MDCK, and BBB) . COSMOperm is a mechanistic method for the prediction of membrane permeability based on quantum chemical solubility calculations. Its basis is the calculation of the free energy profile across the membrane. In general, this approach can calculate logPerm on any membrane. Machine learning (ML) approaches are trained statistically over the existing experimental data, while the data quality and size are extremely important. The QSAR (quantitative structure-activity relationship) model is a mathematical model which identifies statistically significant correlations between the structure of molecules and their properties, such as biological activity . The structure of molecules is described by a variety of descriptors. Choosing the fitting descriptor is one of the key points during the QSAR process. The history of QSAR is long, and countless various permeability QSAR models have been developed, e.g. QSAR models for CACO-2 cell permeability , intestinal permeability , blood-brain barrier (BBB) and skin , etc. QSAR models were recently generalized with ML (machine-learning) models. These models are fitted to train data produced by experimental methods. Experimental methods often used for the construction of these models are CACO-2 (e.g., Wang et al. or Frelund et al. ) and PAMPA (e.g., Sun et al. or Gousiadou et al. ). Sometimes these models are created for a given type of molecule, e.g. cell-penetrating peptides or macrocycles . As their performance can be, in principle, only as good as the original data, we will not discuss them further here.