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Table 4 Nested Leave-one-out MSE Performance on Sutherland Data Sets

From: jCompoundMapper: An open source Java library and command-line tool for chemical fingerprints

Encoding ACE ACHE BZR COX2 DHFR GPB THERM THR
DFS 1.93 0.61 0.61 1.09 0.57 0.66 2.08 0.55
ASP 1.93 0.56 0.54 1.07 0.56 0.63 2.08 0.53
AP2D 1.59 0.79 0.68 1.04 0.72 0.64 2.06 0.45
AT2D 1.68 0.69 0.67 0.92 0.69 0.65 1.92 0.45
CATS2D 1.83 0.83 0.96 1.34 0.65 0.62 2.20 0.45
PHAP2PT2D 1.88 0.92 0.98 1.37 0.67 0.62 2.10 0.46
PHAP3PT2D 1.83 0.91 0.85 1.20 0.66 0.58 2.04 0.50
SHED 2.11 1.00 1.13 1.71 1.41 0.72 2.94 0.43
ECFP 2.01 0.66 0.66 0.96 0.57 0.65 2.17 0.47
RAD2D 1.99 0.73 0.79 1.03 0.72 0.67 2.18 0.43
LSTAR 2.29 0.66 0.68 1.00 0.60 0.71 2.31 0.46
AP3D 1.88 0.64 0.59 0.90 0.67 0.70 2.61 0.54
AT3D 2.04 0.60 0.65 0.97 0.58 0.71 2.70 0.59
CATS3D 1.91 0.85 0.84 1.26 0.70 0.74 2.62 0.58
PHAP2PT3D 1.92 0.81 0.85 1.30 0.70 0.76 2.72 0.62
PHAP3PT3D 2.40 0.73 0.81 1.11 0.59 0.82 2.82 0.67
RAD3 2.43 0.73 0.73 1.04 0.57 0.75 2.75 0.55
  1. Overview of ϵ support vector regression performance on the Sutherland data set using the MinMax kernel function. The performance was evaluated using a nested leave-one-out cross-validation with a parameter optimization using 10-fold cross-validation repeated 2 times. Bold values indicate performances not worse than 10% of the best performing encoding.