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Table 6 Prediction performance of 25 scoring functions evaluated on PDBbind v2007 core set (N = 195) in terms of Pearson correlation coefficient Rp, Spearman correlation coefficient Rs and standard deviation SD in linear correlation on the test set

From: Substituting random forest for multiple linear regression improves binding affinity prediction of scoring functions: Cyscore as a case study

Scoring function Rp Rs SD
RF::CyscoreVinaElem 0.803 0.798 1.42
RF-Score::Elem-v2 0.803 0.797 1.54
SFCscoreRF 0.779 0.788 1.56
RF-Score 0.774 0.762 1.59
ID-Score 0.753 0.779 1.63
RF::CyscoreVina 0.749 0.759 1.58
SVR-Score 0.726 0.739 1.70
RF::Cyscore 0.687 0.694 1.73
Cyscore 0.660 0.687 1.79
X-Score::HMScore 0.644 0.705 1.83
DrugScoreCSD 0.569 0.627 1.96
SYBYL::ChemScore 0.555 0.585 1.98
DS::PLP1 0.545 0.588 2.00
GOLD::ASP 0.534 0.577 2.02
SYBYL::G-Score 0.492 0.536 2.08
DS::LUDI3 0.487 0.478 2.09
DS::LigScore2 0.464 0.507 2.12
GlideScore-XP 0.457 0.435 2.14
DS::PMF 0.445 0.448 2.14
GOLD::ChemScore 0.441 0.452 2.15
SYBYL::D-Score 0.392 0.447 2.19
DS::Jain 0.316 0.346 2.24
GOLD::GoldScore 0.295 0.322 2.29
SYBYL::PMF-Score 0.268 0.273 2.29
SYBYL::F-Score 0.216 0.243 2.35
  1. The scoring functions are sorted in the descending order of Rp. RF::CyscoreVinaElem and Cyscore rank 1st and 9th respectively in terms of Rp. The statistics for the other 21 scoring functions are collected from [8, 22, 31].