Fig. 2: Kinase-drug affinity prediction performance of MMCLKin across two constructed 3D datasets with drug cold-start, kinase cold-start and kinase-drug cold-start splitting strategies.
From: Enhancing kinase-inhibitor activity and selectivity prediction through contrastive learning
a Performance comparison of ConPLex on the constructed 3DKDavis dataset versus the original Davis dataset. Five independent replications of each method were performed (n = 5). Box plots show the median as the center lines, upper and lower quartiles as box limits, whiskers as maximum and minimum values, and circles represent individual data points. b Performance comparison of ConPLex on the constructed 3DKKIBA dataset versus the original KIBA dataset. Five independent replications of each method were performed (n = 5). Box plots show the median as the center lines, upper and lower quartiles as box limits, whiskers as maximum and minimum values, and circles represent individual data points. c Comparison of the kinase-drug affinity prediction performance of MMCLKin against other models across three splitting strategies on the 3DKDavis dataset. Three independent replications of each method were performed (n = 3). Data are expressed as mean ± SD. d Comparison of the kinase-drug affinity prediction performance of MMCLKin against other models across three splitting strategies on the low drug similarity LSKIBA dataset. Three independent replications of each method were performed (n = 3). Data are expressed as mean ± SD. All models were rigorously evaluated using a comprehensive set of performance metrics, including the Concordance Index (CI), Mean Absolute Error (MAE), Pearson Correlation Coefficient (PCC), Mean Squared Error (MSE), and Spearman’s rank correlation coefficient (Spearman). Source data are provided as a Source Data file.
