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Table 5 PPB prediction results of synthetic cyclic peptides for sparse modeling by ELS and FBS compared to the baseline results. These situations are shown in a part of Fig. 1(b). The values with asterisk represent the best prediction performance in each evaluation criterion, and ridge-CP and lasso-CP lines are reproduced from Table 3

From: Computational prediction of plasma protein binding of cyclic peptides from small molecule experimental data using sparse modeling techniques

Method Training set Test set RMSE (fb) MAE (fb) R (ln Ka)
ridge-SM
(baseline #1)
Small molecules (training data) Synthetic cyclic peptides (# = 16) 0.321 0.263 0.761
lasso-SM
(baseline #2)
Small molecules (training data) Synthetic cyclic peptides (# = 16) 0.276 0.228 0.714
ELS Small molecules (training data) Synthetic cyclic peptides (# = 16) 0.319 0.269 0.748
FBS Small molecules (training data) Synthetic cyclic peptides (# = 16) 0.230* 0.194* 0.805*
ridge-CP
(baseline #3)
Cyclic peptide drugs (# = 24) Synthetic cyclic peptides (# = 16) 0.413 0.354 0.442
lasso-CP
(baseline #4)
Cyclic peptide drugs (# = 24) Synthetic cyclic peptides (# = 16) 0.688 0.627 0.069