FIGURE A2.

Median values of R-squared correlations (R²) from four-fold cross validation performances of ${\mathrm{EIC}}_{\mathrm{E},{\kappa }_1,{\tau }_1;\mathrm{E},{\kappa }_2,{\tau }_2}^{CC}$ on the IGC₅₀ training set are plotted against different values of τ₂ and κ₂. Two exponential kernels are utilized for features generation. While the parameters of the first kernel (τ₁, κ₁) are fixed and chosen from those reported in Figure A1, the parameters of the second kernel (τ₂, κ₂) are varied in the interested domains. The best performance for different kinds of curvatures is found as follows: A, minimum curvature: (τ = 0.7, κ = 10), (τ₂ = 0.3, κ₂ = 3.5) with R² = 0.768; B, maximum curvature: (τ = 0.3, κ = 1.0), (τ₂ = 0.3, κ₂ = 3.5) with R² = 0.780; C, Gaussian curvature: (τ = 0.7, κ = 10), (τ₂ = 0.3, κ₂ = 3.5) with R² = 0.745; and D, mean curvature (τ = 0.3, κ = 1.5), (τ₂ = 0.3, κ₂ = 3.5) with R² = 0.772