Fig. 5: A visual representation of how the 15-feature LGBM model generates fractional drug release predictions using the example of 5-FU-PLGA (index 84 in the attached dataset).
From: Machine learning models to accelerate the design of polymeric long-acting injectables
a Experimental fractional drug release profile (orange circles) for 5-FU-PLGA plotted against the predicted fractional drug release profile (blue circles) generated by the LGBM model. b Decision path taken for each fractional drug release prediction for the 5-FU-PLGA system. This plot illustrates how the LGBM model combines the relative contribution of each input feature to return the predicted fractional drug release value. c Shapley additive explanations (SHAP) force plots for the three selected data instances (i.e., fractional drug release prediction 0.01, 0.61, and 0.82) showing a decomposition of predicted fractional drug release values into the relative SHAP contribution values for each input feature. The relative SHAP values for each input feature are shown by pink (positive) or blue (negative) bands on the force plot, with the width of the band representing the numerical contribution to the final model output. Source data are provided as a Source Data file.
