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Table 2 Internal and external validation metrics for the PCM models

From: Proteochemometric modeling in a Bayesian framework

  Adenosine Receptors Dataset  
  R int 2 RMSEPint R 0 ext 2 RMSEPext
GP Bessel 0.64 0.70 0.70 0.63
GP Laplacian 0.67 0.68 0.67 0.66
GP Norm. Polynomial (NP) 0.69 0.65 0.75 0.58
GP Polynomial 0.70 0.64 0.70 0.63
GP PUK 0.57 0.79 0.56 0.77
GP Radial 0.65 0.69 0.65 0.68
PLS 0.29 0.97 0.30 1.00
SVM Norm. Polynomial (NP) 0.70 0.64 0.73 0.60
SVM Polynomial 0.71 0.63 0.71 0.62
SVM Radial 0.68 0.65 0.70 0.64
Family QSAR 0.31 0.70 0.31 0.96
  Aminergic GPCRs Dataset  
  R int 2 RMSEP int R 0 ext 2 RMSEP ext
GP Bessel 0.56 0.83 0.56 0.80
GP Laplacian 0.62 0.78 0.63 0.75
GP Norm. Polynomial (NP) 0.69 0.68 0.72 0.66
GP Polynomial 0.68 0.71 0.70 0.68
GP PUK 0.46 0.93 0.46 0.90
GP Radial 0.69 0.69 0.71 0.66
PLS 0.69 0.69 0.27 1.05
SVM Norm. Polynomial (NP) 0.69 0.68 0.72 0.66
SVM Polynomial 0.69 0.69 0.71 0.66
SVM Radial 0.69 0.69 0.72 0.66
Family QSAR 0.38 0.98 0.38 0.97
  Dengue virus NS3 proteases Dataset  
  R int 2 RMSEP int R 0 ext 2 RMSEP ext
GP Bessel 0.91 0.43 0.92 0.44
GP Laplacian 0.88 0.54 0.91 0.50
GP Linear 0.91 0.45 0.91 0.48
GP Norm. Polynomial (NP) 0.88 0.50 0.91 0.48
GP Polynomial 0.91 0.42 0.92 0.44
GP PUK 0.77 1.10 0.81 1.13
GP Radial 0.91 0.45 0.91 0.45
PLS 0.90 0.45 0.91 0.49
SVM Norm. Polynomial (NP) 0.86 0.54 0.91 0.46
SVM Polynomial 0.89 0.46 0.90 0.51
SVM Radial 0.90 0.48 0.90 0.48
Family QSAR 0.29 1.19 0.48 1.13
  1. For the three datasets, the best models are obtained with GP, being the lowest RMSEPext and highest R20 ext values: (i) adenosine receptors: 0.58 and 0.75 with NP kernel, (ii) GPCRs: 0.66 and 0.72 with NP kernel, and (iii) Dengue virus NS3 proteases 0.44 and 0.92 with Bessel kernel. Overall, GP models for the three datasets agree with the validation criteria.
  2. Abbreviations: RMSEP root mean square error in prediction, Ext. external, Norm Normalized.