Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors
Fig 3
Computational evaluation of the model predictions.
(a) Leave-one-out and (b) leave-drug-out cross-validation results. The prediction accuracy was evaluated with Pearson correlation (r) between binding affinities (pKi) from the study by Metz et al. [3] and those predicted using KronRLS algorithm with different pairs of compound (rows) and protein (columns) molecular descriptors encoded as kernel matrices (c). The corresponding root mean squared error (RMSE) values are shown in S1 Fig. Of note, Gaussian interaction profile drug kernel (KD-GIP), which resulted in the highest predictive performance under the Bioactivity Imputation scenario (a), was not evaluated under the New Drug scenario (b), because it is constructed based on the bioactivity profile of a drug to be predicted, that is, using information that in practice is unavailable when predicting target interactions for a new investigational drug compound.