Machine learning-assisted screening of natural product database for the identification of novel chalcone-based derivative as a potent DHODH inhibitor in cancer therapy

BACKGROUND: Dihydroorotate dehydrogenase (DHODH), an important enzyme in de-novo pyrimidine synthesis, and its dysregulation has been frequently associated with various diseases including cancer. Extensive evidence indicates that the inhibition of DHODH can efficiently induce apoptosis in tumor cells. Although well-known inhibitors like teriflunomide, leflunomide and brequinar have been investigated, their clinical utility is shown to be limited due to poor bioavailability and moderate efficacy in trials. This emphasizes the necessity for the development of potent and non-toxic drug-like candidates targeting DHODH enzyme. Recently, plant-derived compounds offer significant advantage due to their potential of reducing adverse effects compared to synthetic drugs. METHODS: , Vinardo, Smina and X-Score were utilized for validating the compounds activity. RESULTS: Collective evidence highlights that NPACT00730 showed the strongest interactions with the crucial residues such as GLN47, ARG136 and TYR356 of DHODH. Additionally, scaffold analysis revealed that the chalcone moiety present in hit compound is well established with anticancer activity across multiple cancer cell lines. In the end, the results were further supported by membrane simulations for 100ns, followed by solution-based simulation to evaluate the stability of the protein-ligand complex. The parameters such as RMSD, RMSF, Rg, Hydrogen bonds, SASA, Principal component analysis (PCA) and free energy landscape (FEL) were analyzed. CONCLUSION: Overall, we hypothesize that NPACT00730 has inhibitory activity against DHODH and represents a computationally prioritized DHODH inhibitor candidate exhibiting predicted multi-cell-line anticancer sensitivity, warranting further experimental validation.