首页|Nanyang Technological University Reports Findings in Machine Learning (RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning meth od)
Nanyang Technological University Reports Findings in Machine Learning (RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning meth od)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating from Singapore, S ingapore, by NewsRx correspondents, research stated, “Small molecule drugs can b e used to target nucleic acids (NA) to regulate biological processes. Computatio nal modeling methods, such as molecular docking or scoring functions, are common ly employed to facilitate drug design.” Our news editors obtained a quote from the research from Nanyang Technological U niversity, “However, the accuracy of the scoring function in predicting the clos est-to-native docking pose is often suboptimal. To overcome this problem, a mach ine learning model, RmsdXNA, was developed to predict the root-meansquare- devia tion (RMSD) of ligand docking poses in NA complexes. The versatility of RmsdXNA has been demonstrated by its successful application to various complexes involvi ng different types of NA receptors and ligands, including metal complexes and sh ort peptides. The predicted RMSD by RmsdXNA was strongly correlated with the act ual RMSD of the docked poses. RmsdXNA also outperformed the rDock scoring functi on in ranking and identifying closest-to-native docking poses across different s tructural groups and on the testing dataset. Using experimental validated result s conducted on polyadenylated nuclear element for nuclear expression triplex, Rm sdXNA demonstrated better screening power for the RNA-small molecule complex com pared to rDock. Molecular dynamics simulations were subsequently employed to val idate the binding of top-scoring ligand candidates selected by RmsdXNA and rDock on MALAT1. The results showed that RmsdXNA has a higher success rate in identif ying promising ligands that can bind well to the receptor. The development of an accurate docking score for a NA-ligand complex can aid in drug discovery and de velopment advancements.”
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