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Table 2 Comparison results of the proposed method and baselines in the compound cold start setting

From: Bridging chemical structure and conceptual knowledge enables accurate prediction of compound-protein interaction

Method

AUC

AUPR

Precision

Recall

ACC

LR

\(\text {0.799}_{(\text {-})}\)

\(\text {0.826}_{(\text {-})}\)

\(\text {0.790}_{(\text {-})}\)

\(\text {0.717}_{(\text {-})}\)

\(\text {0.753}_{(\text {-})}\)

DeepDTI

\(\text {0.821}_{(\pm \text {0.005})}\)

\(\text {0.849}_{(\pm \text {0.003})}\)

\({\mathit{0.866}}_{\mathit{(\pm0.007)}}\)

\(\text {0.585}_{(\pm \text {0.028})}\)

\(\text {0.742}_{(\pm \text {0.010})}\)

DeepConv-DTI

\(\text {0.853}_{(\pm \text {0.004})}\)

\(\text {0.876}_{(\pm \text {0.003})}\)

\(\text {0.741}_{(\pm \text {0.020})}\)

\({\textbf {0.819}}_{(\pm \text {0.013})}\)

\(\text {0.755}_{(\pm \text {0.012})}\)

MolTrans

\(\text {0.843}_{(\pm \text {0.011})}\)

\(\text {0.863}_{(\pm \text {0.008})}\)

\(\text {0.796}_{(\pm \text {0.016})}\)

\(\text {0.730}_{(\pm \text {0.041})}\)

\(\text {0.761}_{(\pm \text {0.012})}\)

TransformerCPI

\(\text {0.855}_{(\pm \text {0.003})}\)

\(\text {0.871}_{(\pm \text {0.006})}\)

\(\text {0.797}_{(\pm \text {0.025})}\)

\(\text {0.781}_{(\pm \text {0.025})}\)

\(\text {0.781}_{(\pm \text {0.006})}\)

DrugBAN

\(\mathit{0.880}_{\mathit{(\pm0.004)}}\)

\(\mathit{0.900}_{\mathit{(\pm0.003)}}\)

\(0.834_{(\pm 0.008)}\)

\(0.791_{(\pm 0.006)}\)

\(\mathit{0.809}_{\mathit{(\pm0.002)}}\)

BEACON

\({\textbf {0.924}}_{(\pm \text {0.003})}\)

\({\textbf {0.937}}_{(\pm \text {0.002})}\)

\({\textbf {0.884}}_{(\pm \text {0.006})}\)

\({\mathit{0.802}}_{\mathit{(\pm0.015)}}\)

\({\textbf {0.841}}_{(\pm \text {0.003})}\)

  1. The boldface denotes the highest score, and italics indicate the second highest score