De novo designing, assessment of target affinity and binding interactions against aromatase: Discovery of novel leads as anti-breast cancer agents

Abstract

Aromatase inhibitors (AIs) have been emerged as promising anti-cancer agents for the treatment of hormone dependent breast cancer (HDBC) in women because of their excellent ability of inhibiting oestrogen synthesis. Here, we have implicated structure-based comprehensive approaches to discover novel drug/lead-like AIs. The molecular modelling and energy optimization were performed using Chem Office package. The e-LEA3D web server was used to design novel drug/lead-like AIs as well as generation of ADME/drug-likeness parameters. Target binding affinities and mode of binding interactions were mapped using Molegro Virtual Docker (MVD) to re-optimize the best de novo generated molecules. We have successfully designed novel AIs (compounds 1�7) using de novo technique performed on e-LEA3D. All the designed molecules were found optimum drug-like candidates based on various in silico screening parameters including �rule of five�. The energy optimized conformers of generated molecules (1�7) were docked in the active site, corresponding to co-crystallized androstenedione (ASD), of aromatase to predict ligand-target binding affinity and their binding interactions. The molecules (1�7) showed comparable to higher binding affinity towards aromatase with MolDock Score ranges from ? 134.881 to ? 152.453�Kcal/mol as compared with natural substrate ASD (? 128.639�Kcal/mol) and standard letrozole (? 136.784�Kcal/mol). The de novo designed molecules (1�7) can be developed as novel AIs, and their binding properties can be used for the further designing of newer AIs by medicinal chemists. � 2020, Springer Science+Business Media, LLC, part of Springer Nature.