Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors.

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Computational Bioactivity Fingerprint Similarities To Navigate the Discovery of Novel Scaffolds.

As one of the central tasks of modern medicinal chemistry, scaffold hopping is expected to lead to the discovery of structural novel biological active compounds and broaden the chemical space of known active compounds. Here, we report the computational bioactivity fingerprint (CBFP) for easier scaffold hopping, where the predicted activities in multiple quantitative structure-activity relationship models are integrated to characterize the biological space of a molecule. In retrospective benchmarks, the CBFP representation shows outstanding scaffold hopping potential relative to other chemical descriptors. In the prospective validation for the discovery of novel inhibitors of poly [ADP-ribose] polymerase 1, 35 predicted compounds with diverse structures are tested, 25 of which show detectable growth-inhibitory activity; beyond this, the most potent (compound 6) has an IC50 of 0.263 nM. These results support the use of CBFP representation as the bioactivity proxy of molecules to explore uncharted chemical space and discover novel compounds.

The ups and downs of Poly(ADP-ribose) Polymerase-1 inhibitors in cancer therapy-Current progress and future direction.

Poly(ADP-ribose) Polymerase 1 (PARP1), one of the most investigated 18 membered PARP family enzymes, is involved in a variety of cellular functions including DNA damage repair, gene transcription and cell apoptosis. PARP1 can form a PARP1(ADP-ribose) polymers, then bind to the DNA damage gap to recruit DNA repair proteins, and repair the break to maintain genomic stability. PARP1 is highly expressed in tumor cells, so the inhibition of PARP1 can block DNA repair, promote tumor cell apoptosis, and exert antitumor activity. To date, four PARP1 inhibitors namely olaparib, rucaparib, niraparib and talazoparib, have been approved by Food and Drug Administration (FDA) for treating ovarian cancer and breast cancer with BRCA1/2 mutation. These drugs have showed super advantages over conventional chemotherapeutic drugs with low hematological toxicity and slowly developed drug resistance. In this article, we summarize and analyze the structure features of PARP1, the biological functions and antitumor mechanisms of PARP1 inhibitors. Importantly, we suggest that establishing a new structure-activity relationship of developed PARP1 inhibitors via substructural searching and the matched molecular pair analysis would accelerate the process in finding more potent and safer PARP1 inhibitors.