Integrating machine learning driven virtual screening and molecular dynamics simulations to identify potential inhibitors targeting PARP1 against prostate cancer.

Prostate cancer (PC) is one of the most common types of malignancies in men, with a noteworthy increase in newly diagnosed cases in recent years. PARP1 is a ubiquitous nuclear enzyme involved in DNA repair, nuclear transport, ribosome synthesis, and epigenetic bookmarking. In this study, a library of 9000 phytochemicals was screened, with a focus on those with high drug efficacy and potential PARP1 inhibition. Different machine learning models were generated and assessed using various statistical measures. The RF model outperformed all other models in terms of accuracy (0.9489), specificity (0.9171), and area under the curve (AUC = 0.9846). Following this, a library of 9510 phytochemicals was screened, yielding 181 compounds predicted to be active. These compounds were subsequently assessed using Lipinski's Rule of Five, yielding 40 interesting candidates. Molecular docking experiments demonstrated that compound ZINC2356684563, ZINC2356558598, and ZINC14584870, had strong affinity for the PARP1 active site. Further molecular dynamics simulations and MM-PBSA validated the stability of the ligand-protein complexes, with ZINC14584870 and ZINC43120769 demonstrating the most stable interaction, as seen by low RMSD and RMSF levels. Our findings emphasize the potential of these phytochemical inhibitors as novel therapeutic agents against PARP1 in prostate cancer treatment, paving the path for further experimental validation and clinical investigations. These results open new possibilities for developing treatments to benefit prostate cancer patients.