Discovery of benzimidazole-based Mcl-1 inhibitors via AlphaShape-enabled virtual screening

As an essential regulator of tumor cell survival mechanisms, myeloid cell leukemia 1 (Mcl-1) represents a promising anti-cancer target. This study describes the identification of novel benzimidazole scaffolds targeting Mcl-1 through AlphaShape, a deep neural network-empowered shape-based screening program. Through structure-based optimization of the initial hit compound, we developed a series of derivatives exhibiting enhanced binding specificity for Mcl-1 over Bcl-2/Bcl-xL. Notably, compounds 26c and 26d demonstrated submicromolar binding affinities (Ki = 0.59 and 0.74 μM, respectively) with concomitant antiproliferative effects in pancreatic cancer cells through apoptosis induction. Furthermore, the binding mode of 26d was elucidated through an integrated approach combining molecular dynamics simulation and HSQC-NMR spectroscopy. Our findings not only validate AlphaShape as an efficient tool for lead discovery but also provide a strategic framework for developing targeted Mcl-1 inhibitors with therapeutic potential in pancreatic cancer treatment.