Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.ĭue to the vast improvement of computing power and the rapid development of computational chemistry and biology, computer-aided drug design (CADD) technology has been successfully applied in drug discovery, accelerating the research process and reducing the associated costs and risks. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of TβR1 inhibitors. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function.
Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of TβR1, with a successful docking rate of 95%. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein–ligand complexes.
#Cdock 201050 software
To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study.
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