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Table 11 Mean Δ EF values and standard deviations for the MCS and MCS ext similarity methods (approach B2)

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

9.1 ± 2.5

6.5 ± 6.1

2.0 ± 2.8

ER

13.6 ± 7.6

12.6 ± 4.1

5.3 ± 1.7

10.2 ± 6.5

10.6 ± 2.5

5.0 ± 1.0

PPAR γ

4.6 ± 10.6

1.2 ± 5.4

1.7 ± 2.8

4.5 ± 11.0

3.8 ± 5.5

1.6 ± 2.9

P38 MAP

9.6 ± 7.9

8.6 ± 3.7

3.3 ± 1.8

7.1 ± 6.8

7.3 ± 3.7

2.7 ± 2.0

TK

20.1 ± 4.4

12.6 ± 2.1

5.1 ± 1.6

19.7 ± 5.3

14.0 ± 2.1

5.5 ± 1.5

FXa

4.6 ± 11.2

7.6 ± 3.8

3.3 ± 1.8

3.5 ± 11.2

6.2 ± 4.7

2.5 ± 2.6

ADA

10.1 ± 6.4

8.2 ± 3.0

4.3 ± 3.6

12.8 ± 6.4

8.8 ± 3.3

6.1 ± 4.6

DHFR

10.9 ± 10.6

11.7 ± 2.9

4.7 ± 1.1

3.4 ± 7.0

2.4 ± 2.1

0.1 ± 0.1

AChE

10.3 ± 12.5

11.3 ± 4.7

4.8 ± 2.5

10.1 ± 11.9

10.4 ± 5.1

4.4 ± 3.0

COX-2

12.3 ± 9.2

11.7 ± 2.2

5.3 ± 1.1

11.4 ± 10.3

10.5 ± 3.7

2.5 ± 2.5

w/d/l

 

8 / 0 / 2

7 / 0 / 3

8 / 0 / 2

  1. Mean Δ EF and standard deviations 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 10% (20% for HMGR, TK and ADA) of the ligands (approach B2). Improvements of MCS ext compared to MCS are marked with bold print. w/d/l = wins/draws/losses.