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Table 16 Mean Δ EF and standard deviation using the best α coefficients (approach B2)

From: Improving structural similarity based virtual screening using background knowledge

 

MCS ext

ECFP ext

DuD set

1%

5%

10%

1%

5%

10%

HMGR

6.1 ± 10.5

2.1 ± 0.9

0.8 ± 0.4

2.7 ± 6.8

4.3 ± 5.6

1.5 ± 2.5

ER

8.1 ± 6.1

10.4 ± 2.9

4.6 ± 1.5

6.3 ± 5.4

10.0 ± 2.0

4.2 ± 1.3

PPAR γ

4.6 ± 10.6

3.9 ± 5.5

1.7 ± 2.8

4.1 ± 10.7

3.5 ± 5.6

1.7 ± 2.5

P38 MAP

5.4 ± 6.4

5.4 ± 3.4

1.4 ± 0.1

3.9 ± 5.7

6.7 ± 3.8

1.4 ± 0.1

TK

17.4 ± 5.2

11.4 ± 4.8

4.7 ± 1.6

16.5 ± 8.4

11.4 ± 3.5

4.6 ± 2.1

FXa

3.5 ± 11.2

5.7 ± 5.1

2.5 ± 2.6

3.5 ± 11.0

5.0 ± 5.2

2.2 ± 2.6

ADA

9.7 ± 5.4

7.2 ± 2.6

2.4 ± 1.1

7.3 ± 7.2

6.9 ± 2.7

2.4 ± 1.0

DHFR

3.0 ± 6.0

0.8 ± 1.6

0.0 ± 0.0

2.4 ± 1.2

0.4 ± 0.9

0.0 ± 0.0

ACHE

10.1 ± 12.0

9.6 ± 5.8

4.5 ± 2.9

11.2 ± 12.2

9.4 ± 5.9

4.4 ± 2.8

COX-2

12.0 ± 10.3

10.8 ± 6.3

2.8 ± 2.8

6.7 ± 10.3

5.5 ± 4.9

2.2 ± 2.6

w/d/l

9 / 1 / 0

9 / 0 / 1

9 / 1 / 0

8 / 0 / 2

4 / 0 / 6

8 / 0 / 2

  1. Mean Δ EF and standard deviation using the best α coefficients for extended similarites MCS ext and ECFP ext for the receptor specific decoy sets DuD set at 1%, 5% and 10% of the database. 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 as well as ECFP ext compared to ECFP are marked in bold print. w/d/l = wins/draws/losses.