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Molecular Modeling Studies on Benzimidazole Carboxamide Derivatives as PARP‐1 Inhibitors Using 3D‐QSAR and Docking
Author(s) -
Zeng Huahui,
Zhang Huabei,
Jang Fubin,
Zhao Lingzhou,
Zhang Jianyuan
Publication year - 2011
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2011.01139.x
Subject(s) - benzimidazole , quantitative structure–activity relationship , docking (animal) , chemistry , carboxamide , stereochemistry , computational biology , combinatorial chemistry , pharmacology , biology , medicine , organic chemistry , nursing
Poly(ADP‐ribose) polymerases (PARPs) play significant roles in various cellular functions including DNA repair and control of RNA transcription. PARP‐1 inhibitors have been demonstrated to potentiate the effect of cytotoxic agents or radiation in a number of animal tumor models. To understand the structure–activity correlation of cyclic amine‐containing benzimidazole carboxamide‐based PARP‐1 inhibitors, we have carried out a combined molecular docking and three‐dimensional quantitative structure–activity relationship (3D‐QSAR) modeling study. Two types of satisfactory substructure‐based 3D‐QSAR models were built, including the comparative molecular field analysis (CoMFA) model ( r 2 , 0.913; q 2 , 0.743) and comparative molecular similarity indices analysis (CoMSIA) model ( r 2 , 0.869; q 2 , 0.734), to predict the biologic activity of new compounds. Docking studies were performed to explore the binding mode between all of the inhibitors and the PARP‐1 and produce the bioactive conformation of each compound in the whole data set. The docked conformer‐based alignment strategy gave the best 3D‐QSAR models, CoMFA model ( r 2 , 0.899; q 2 , 0.712) and CoMSIA model ( r 2 , 0.889; q 2 , 0.744), respectively. The structural insights obtained from both the 3D‐QSAR contour maps and molecular docking help to better interpret the structure–activity relationship. The information obtained from molecular modeling studies helped us to predict the activity of new inhibitors and further design some novel and potent PARP‐1 enzyme inhibitors.