Abstract
Poly (ADP-ribose) polymerase 1 (PARP1) is an important enzyme that plays a central role
in the DNA damage response, facilitating repair of single-stranded DNA breaks via the
base excision repair (BER) pathway and thus genomic integrity. Its therapeutic relevance is
compounded in breast cancer, particularly in BRCA1 or BRCA2 mutant cancers, where compromised
homologous recombination repair (HRR) leaves a synthetic lethal dependency
on PARP1-mediated repair. This review comprehensively discusses the recent advances in
computational chemistry for the discovery of PARP1 inhibitors, focusing on their application
in breast cancer therapy. Techniques such as molecular docking, molecular dynamics
(MD) simulations, quantitative structure–activity relationship (QSAR) modeling, density
functional theory (DFT), time-dependent DFT (TD-DFT), and machine learning (ML)-aided
virtual screening have revolutionized the discovery of inhibitors. Some of the most prominent
examples are Olaparib (IC50 = 5 nM), Rucaparib (IC50 = 7 nM), and Talazoparib
(IC50 = 1 nM), which were optimized with docking scores between −9.0 to −9.3 kcal/mol
and validated by in vitro and in vivo assays, achieving 60–80% inhibition of tumor growth
in BRCA-mutated models and achieving up to 21-month improvement in progression-free
survival in clinical trials of BRCA-mutated breast and ovarian cancer patients. These strategies
enable site-specific hopping into the PARP1 nicotinamide-binding pocket to enhance
inhibitor affinity and specificity and reduce off-target activity. Employing computation
and experimental verification in a hybrid strategy have brought next-generation inhibitors
to the clinic with accelerated development, higher efficacy, and personalized treatment
for breast cancer patients. Future approaches, including AI-aided generative models and
multi-omics integration, have the promise to further refine inhibitor design, paving the
way for precision oncology.