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Table 8 Mean Δ EF and standard deviation for the MCS and MCS ext similarity methods (approach B1)

From: Improving structural similarity based virtual screening using background knowledge

DuD set MCS MCS ext
  1% 5% 10% 1% 5% 10%
HMGR 8.5 ± 4.5 7.0 ± 6.8 2.8 ± 3.6 4.6 ± 9.8 2.0 ± 1.0 0.5 ± 0.3
ER 13.6 ± 7.6 12.6 ± 4.1 5.3 ± 1.7 13.1 ± 7.0 11.4 ± 2.8 3.7 ± 0.9
PPAR γ 4.6 ± 10.6 1.2 ± 5.4 1.7 ± 2.8 4.6 ± 11.0 3.8 ± 5.5 1.5 ± 2.9
P38 MAP 9.6 ± 7.9 8.6 ± 3.7 3.3 ± 1.8 3.8 ± 5.4 4.8 ± 4.2 2.4 ± 2.1
TK 20.1 ± 4.4 12.6 ± 2.1 5.1 ± 1.6 18.3 ± 5.3 12.3 ± 2.7 4.0 ± 1.3
FXa 4.6 ± 11.2 7.6 ± 3.8 3.3 ± 1.8 3.5 ± 11.0 6.4 ± 4.6 2.5 ± 2.5
ADA 10.1 ± 6.4 8.2 ± 3.0 4.3 ± 3.6 9.2 ± 4.8 7.7 ± 2.0 2.3 ± 0.8
DHFR 10.9 ± 10.6 11.7 ± 2.9 4.7 ± 1.1 3.1 ± 5.0 0.3 ± 0.3 0.0 ± 0.0
AChE 10.3 ± 12.5 11.3 ± 4.7 4.8 ± 2.5 10.0 ± 11.8 9.5 ± 5.8 4.4 ± 3.0
COX-2 12.3 ± 9.2 11.7 ± 2.2 5.3 ± 1.1 10.7 ± 10.3 10.1 ± 3.8 2.2 ± 2.6
w/d/l   10 / 0 / 0 9 / 0 / 1 10 / 0 / 0
  1. Mean Δ EF and standard deviation for the MCS and MCS ext similarity methods at 1%, 5% and 10% of the database (receptor specific decoy set DuD set ). The extension fingerprint is calculated from all ligands (approach B1). Improvements of MCS ext compared to MCS are marked with bold print. w/d/l = wins/draws/losses.