The pathogenicity of human variants is an important annotation feature that may help in understanding, at a molecular level, the propensity for a human being to develop a certain disease or pathology. Recently, protein sequence embedding associated with machine and/or deep learning has been proven useful in improving results in this area. Different aspects of pathogenic variants can help in understanding the molecular mechanisms of the disease at a molecular level. These include solvent accessibility in the folded gene, the effect on the protein stability, and eventually the perturbation on interaction networks important for biological processes. Here, we describe how, once a variant is predicted "pathogenic", other important structural and functional properties can be derived computationally at the same website ( https://bioinformaticsweeties.biocomp.unibo.it/ ), including the protein structure, if not available. All the properties can help to understand variant effects within the complex context of the cell environment.