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Table 7 Comparison of all baseline approaches and ELECTRA-DTA on the Davis datasets

From: ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding

Dataset

Model

CI

MSE

R

\(r_m^2\)

AUPR

Original Davis Dataset

KronRLS

0.871

0.379

0.407

0.661

SimBoost

0.872

0.282

0.644

0.709

DeepDTA

0.876 (0.004)

0.261

0.846

0.630 (0.017)

0.714 (0.010)

WideDTA

0.886

0.262

DeepCDA

0.891 (0.003)

0.248

0.857

0.649 (0.009)

0.739 (0.006)

ELECTRA-DTA

0.897 (0.003)

0.238

0.844

0.671 (0.032)

0.698 (0.010)

refined Davis Dataset

DeepDTA

0.882 (0.016)

0.191

0.843

0.690 (0.035)

0.695 (0.03)

Attention-DTA

0.888 (0.007)

0.195

0.836

0.697 (0.005)

0.677 (0.022)

ELECTRA-DTA

0.896 (0.002)

0.195

0.838

0.637 (0.048)

0.685 (0.026)

  1. Bold values represent the best performance over all competitive methods