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Table 4 PPB prediction results of cyclic peptide drugs 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-LOO and lasso-CP-LOO 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) Cyclic peptide drugs (# = 24) 0.528 0.442 0.120
lasso-SM
(baseline #2)
Small molecules (training data) Cyclic peptide drugs (# = 24) 0.321 0.251 0.444
ELS Small molecules (training data) Cyclic peptide drugs (# = 24) 0.272* 0.216* 0.464*
FBS Small molecules (training data) Cyclic peptide drugs (# = 24) 0.381 0.288 0.270
ridge-CP-LOO
(baseline #3)
Cyclic peptide drugs (LOOCV) Cyclic peptide drugs (LOOCV) 0.338 0.244 0.418
lasso-CP-LOO
(baseline #4)
Cyclic peptide drugs (LOOCV) Cyclic peptide drugs (LOOCV) 0.358 0.286 0.289