Discovering Dually Active Anti-cancer Compounds with a Hybrid AI-structure-based Approach @JCIM_JCTC 1/ In this study, a novel hybrid AI-structure-based approach was developed to identify compounds that can target both G-quadruplex (G4) structures and the poly(ADP-ribose) polymerase (PARP1) enzyme, opening the door to innovative cancer therapies with dual action. 2/ The innovative two-step virtual screening protocol integrates machine learning and structure-based methods. PyRMD, an AI-powered tool, identifies potential PARP1 inhibitors from vast chemical libraries, then evaluates them for their G4 stabilization potential. 3/ By combining AI and structure-based screening, the study identifies four compounds with dual PARP1 inhibition and G4 stabilization activity, offering a multipronged strategy to disrupt cancer cell proliferation. 4/ The identified compounds show significant antiproliferative activity, particularly against cancer cell lines with BRCA1 mutations, suggesting the potential for targeting tumors with DNA repair deficiencies. 5/ One compound, in particular, demonstrated activity against telomerase-positive HeLa cells, hinting at its broader applicability across different cancer types, making it a versatile therapeutic candidate. 6/ This study showcases the potential of hybrid AI-driven approaches in drug discovery, emphasizing the power of combining machine learning with classical structure-based methods to develop multitarget anticancer compounds. @scoscona 📜Paper: doi.org/10.1021/acs.jcim.4c0…