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Table 1 Performance of \(\Delta\)-AEScore compared to the \(\Delta _\text {vina}\text {RF}\) for affinity prediction on the CASF-2013 and CASF-2016 benchmarks. For \(\Delta\)-AEScore the “hard overlap” between the training and both test sets is removed while for \(\Delta _\text {vina}\text {RF}\) only the “hard overlap” between the training set and CASF-2013 is removed [49, 67]. The best performance for each test set is underlined. RMSE values are given in pK units

From: Learning protein-ligand binding affinity with atomic environment vectors

Model

Training set

Test set

RMSE

Pearson’s r

\(\Delta\)-AEScore\(^{\dag }\)

Refined 2013

CASF-2013

1.53

0.74

\(\Delta\)-AEScore\(^{\dag }\) (no H)

Refined 2013

CASF-2013

1.52

0.74

\(\Delta _\text {vina}\text {RF}\) [49]

Refined 2013

CASF 2013

0.69

Vina (optim)

CASF-2013

1.82

0.61

\(\Delta\)-AEScore\(^{\dag }\)

Refined 2016

CASF-2016

1.34

0.79

\(\Delta\)-AEScore\(^{\dag }\) (no H)

Refined 2016

CASF-2016

1.32

0.80

\(\Delta _\text {vina}\text {RF}\) [40, 49]

Refined 2013

CASF 2016

0.81

Vina (optim)

CASF-2016

1.75

0.59

  1. \(^\dag\) This work