Tankyrase enzymes (TNKS), a core part of the canonical Wnt pathway, are a promising\ntarget in the search for potential anti-cancer agents. Although several hundreds of the TNKS inhibitors\nare currently known, identification of their novel chemotypes attracts considerable interest. In this\nstudy, the molecular docking and machine learning-based virtual screening techniques combined\nwith the physico-chemical and ADMET (absorption, distribution, metabolism, excretion, toxicity)\nprofile prediction and molecular dynamics simulations were applied to a subset of the ZINC database\ncontaining about 1.7 M commercially available compounds. Out of seven candidate compounds\nbiologically evaluated in vitro for their inhibition of the TNKS2 enzyme using immunochemical\nassay, two compounds have shown a decent level of inhibitory activity with the IC50 values of\nless than 10 nM and 10 microM. Relatively simple scores based on molecular docking or MM-PBSA\n(molecular mechanics, Poisson-Boltzmann, surface area) methods proved unsuitable for predicting\nthe effect of structural modification or for accurate ranking of the compounds based on their binding\nenergies. On the other hand, the molecular dynamics simulations and Free Energy Perturbation (FEP)\ncalculations allowed us to further decipher the structure-activity relationships and retrospectively\nanalyze the docking-based virtual screening performance. This approach can be applied at the\nsubsequent lead optimization stages.
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