Skip to main content

Table 2 DDI Retrospective evaluation: training in an earlier version of DrugBank and testing in a more updated version of DrugBank. KMR correctly predicts up to 92.19% of the DDIs found after 2016

From: KMR: knowledge-oriented medicine representation learning for drug–drug interaction and similarity computation

  Accuracy Precision Recall F-score AUROC AUPR
FBK-irst 0.6533 0.6437 0.6867 0.6645 0.6807 0.7479
SVM 0.7867 0.7622 0.8333 0.7962 0.8844 0.8694
CNN 0.81 0.8039 0.82 0.8118 0.8892 0.8897
Att-BLSTM 0.7750 0.7749 0.7750 0.7750 0.8455 0.8486
Tiresias 0.80 0.7885 0.82 0.8039 0.8869 0.8861
LP-AllSim 0.77 0.7547 0.8 0.7767 0.8544 0.8600
KMR (our model) 0.9219 0.9191 0.9191 0.9191 0.9512 0.9568
w/o pharmacology 0.8571 0.8570 0.8571 0.8571 0.8571 0.8571
w/o drug class 0.8854 0.8854 0.8855 0.8854 0.8854 0.9391
w/o textual description 0.9033 0.9032 0.9033 0.9033 0.9373 0.9432
  1. Results in italics identify the best values for the testing
Back to article page

Journal of Cheminformatics

ISSN: 1758-2946

Contact us