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Computational chemistry offers variety of tools to study properties of biological macromolecules. These tools vary in terms of levels of details from quantum mechanical treatment to numerous macroscopic approaches. Here, we provide a review of computational chemistry algorithms and tools for modeling the effects of genetic variations and their association with diseases. Particular emphasis is given on modeling the effects of missense mutations on stability, conformational dynamics, binding, hydrogen bond network, salt bridges, and pH-dependent properties of the corresponding macromolecules. It is outlined that the disease may be caused by alteration of one or several of above-mentioned biophysical characteristics, and a successful prediction of pathogenicity requires detailed analysis of how the alterations affect the function of involved macromolecules. The review provides a short list of most commonly used algorithms to predict the molecular effects of mutations as well.