A novel generative framework for designing pathogen-targeted antimicrobial peptides with programmable physicochemical properties
Fig 5
Evaluation of the conditional generative ability.
(A) Conditional Generation. The y-axis represents normalized property values. (B) Unconditional Generation. (C) Comparison of MIC distributions. This panel compares the predicted MIC distributions of model-generated peptides with those of the positive and negative samples from the training set. (D) Comparison of the resulting physicochemical properties of generated peptide sequences under two different target property specifications. The red boxes highlight regions where differences exist between the two property groups. (E) Adjusting the properties of Cecropin and comparing the distributional differences in the generated peptides. (F) Enhancement of MIC for Cecropin. The left panel shows results against E. coli, and the right panel shows results against S. aureus.