Advances in graphics processing units (GPUs) made it possible to achieve great results in the virtual screening problem. Due to the larger number of computational units and the specificity of the problem, GPUs can provide better performance of binding energy optimization than CPUs . For example, programs such as ABMER, GROMACS and CHARMM have been rewritten using NVidia's CUDA (Computer Unified Device Architecture). This approach makes it possible to process the same amount of information as a CPU cluster . For molecular mechanics methods running on GPUs, the splitting method is used, in which the calculations of independent interactions are transferred to the GPU, and the rest of the calculations are transferred to the CPU. Other methods have also been optimised for CUDA: ab initio (GAMESS, Firefly), semi-empirical (MOPAC), FEP (Free Energy Perturbation) and similarity search (FastROCS) .