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Image Search Results
Journal: Journal of computational chemistry
Article Title: Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
doi: 10.1002/jcc.24667
Figure Lengend Snippet: 20 Features in Δ vina RF 20
Article Snippet: In the AutoDock Vina source code [ 47 ] , there are a total of 58 terms implemented as listed in Table S2 , among which 6 terms have been selected for the
Techniques:
Journal: Journal of computational chemistry
Article Title: Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
doi: 10.1002/jcc.24667
Figure Lengend Snippet: Performance of 22 scoring functions in (A) scoring power measured by Pearson’s R, (B) ranking power in terms of high-level success rate and (C) docking power measured by the success rate when the best-scored pose is considered to match the native pose in CASF-2013 benchmark. ΔvinaRF20 is colored in red and AutoDock Vina is colored in green. All results colored in blue are obtained from reference[13].
Article Snippet: In the AutoDock Vina source code [ 47 ] , there are a total of 58 terms implemented as listed in Table S2 , among which 6 terms have been selected for the
Techniques:
Journal: Journal of computational chemistry
Article Title: Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
doi: 10.1002/jcc.24667
Figure Lengend Snippet: Performance of 22 scoring functions in screening power measured by (A) enrichment factor and (B) success rate at top 1% level in CASF-2013 benchmark. ΔvinaRF20 is colored in red and AutoDock Vina is colored in green. All results colored in blue are obtained from reference[13].
Article Snippet: In the AutoDock Vina source code [ 47 ] , there are a total of 58 terms implemented as listed in Table S2 , among which 6 terms have been selected for the
Techniques:
Journal: Journal of computational chemistry
Article Title: Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
doi: 10.1002/jcc.24667
Figure Lengend Snippet: Performance of 18 scoring functions in (A) scoring power measured by Pearson’s R, (B) ranking power in terms of high-level success rate and (C) docking power measured by the success rate when the best-scored pose is considered to match the native pose in CASF-2007 benchmark. ΔvinaRF20 is colored in red and AutoDock Vina is colored in green. All results colored in blue are obtained from reference[12].
Article Snippet: In the AutoDock Vina source code [ 47 ] , there are a total of 58 terms implemented as listed in Table S2 , among which 6 terms have been selected for the
Techniques:
Journal: Journal of computational chemistry
Article Title: Improving Scoring-Docking-Screening Powers of Protein-Ligand Scoring Functions using Random Forest
doi: 10.1002/jcc.24667
Figure Lengend Snippet: CASF-2013 benchmark test performance of AutoDock Vina (colored in green), scoring function developed with RF approach (colored in blue) using experimental data alone and the same twenty features in ΔvinaRF20 scoring function, and scoring functions developed with RF approach and ΔvinaRF approach (colored in red) using the same twenty features and the same training set for the development of the ΔvinaRF20 scoring function. (A) Scoring power; (B) Docking power; (C) Screening power. Each set is run 10 times with different random seed for random forest and calculated by averaging over 10 performances except AutoDock Vina. The AutoDock Vina performance is also indicated by dashed line.
Article Snippet: In the AutoDock Vina source code [ 47 ] , there are a total of 58 terms implemented as listed in Table S2 , among which 6 terms have been selected for the
Techniques: