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Research Article

ECB. 2022; 11(5): 22-30


IDENTIFYING NEW 9-ANILINOACRIDINE-BASED PARP1 INHIBITORS USING TEXT MINING AND INTEGRATED MOLECULAR MODELING APPROACHES

Baliwada Aparna, Kalirajan Rajagopal, Potlapati Varakumar, Kannan Raman, Gowramma Byran, Srikanth Jupudi.




Abstract

The PARP research on cancer and ischemia is advancing at a breakneck pace. Olaparib, Rucaparib, Niraparib, and
Talazoparib are the four PARP1 inhibitors currently on the market, according to the FDA. All of these compounds are non-selective
PARP1 inhibitors. Novel and selective PARP1 inhibitors are desperately needed right now. A small molecule database (Specs SC)
was used to find new selective lead inhibitors of PARP1 in this study. The 9-anilinoacridine scaffold is a new fragment that is
employed as a PARP1 inhibitor and anti-proliferative drug. Thus, 21 compounds containing 9-Anilinoacridine fragments were
discovered and virtually tested in the binding site of target protein PARP1 based on text mining studies. In molecular dynamics
(MD) simulations, compounds with high docking scores were employed. The anticipated binding energies were compared to known
PARP1 inhibitors using free energy calculations. Docking study revealed that among all 21 compounds 1v showed highest g score.
Prime MMGBSA analysis gave the relative binding energies of 1v. The essential amino acid interactions of these newly discovered
hits in the binding pocket were also studied in depth in order to gain a better understanding of the structural properties required for
next-generation PARP1 inhibitors. Thus, we identified novel 9-Anilinoacridine-based hits against the PARP1 enzyme using a mix
of text-mining and integrated molecular modelling techniques.

Key words: PARP1 inhibitors; virtual screening; text mining; molecular docking; molecular dynamics simulations; Pharmacophore; 9-Anilino acridine derivatives.






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