Small molecules can interact with biological macromolecules and thus affect the organism's homeostasis. Within this doctoral dissertation, selected chemical compounds were theoretically investigated as potential antimicrobial agents and/or cholinesterase inhibitors. The most important interactions of small molecules with macromolecules were determined by molecular docking methods: 1. newly developed method of tensor decomposition of ab initio molecular dynamics trajectories and 2. quantum-chemical Monte Carlo docking within the active site of the enzyme. Molecular docking by ab initio molecular dynamics includes a machine learning protocol for determining the length of simulations where complete configuration spaces are sampled. Quantum-chemical Monte Carlo docking implies sampling of structures through the generation of random system configurations (enzyme active site and small molecule) and quantum-chemical energy calculations. Through the extensive application of machine learning, the best possible multivariate linear regression models that connect the biological activity of the observed compounds with their theoretically sampled potential energy surfaces were established. The obtained results will represent the basis for further research of the most important interactions of small molecules with cholinesterases as well as the prediction of bioactivity and the "smart" design of new bioactive molecules