Classifier-driven generative adversarial networks for enhanced antimicrobial peptide design.

The development of antimicrobial peptides (AMPs) presents a promising approach to addressing antibiotic-resistant pathogens. Computational methods, such as Feedback Generative Adversarial Networks (FBGANs), have demonstrated strong performance in optimizing AMP design. FBGAN operates as a classifier-guided Generative Adversarial Network (GAN), refining training data by replacing them with the classifier's most accurate predictions based on a predefined threshold. However, this method may introduce bias and constrain the diversity and quality of the generated peptides. To address these limitations, we propose a novel classifier-driven GAN (cdGAN) framework that seamlessly integrates classifier predictions into the generative model's loss function. This enables an adaptive, end-to-end learning process that enhances AMP generation without requiring explicit data modifications. By embedding classifier guidance within the loss computation, cdGAN dynamically optimizes both peptide diversity and functionality. Comparative studies indicate that cdGAN outperforms conventional guided-GAN architectures, such as Conditional GANs and Auxiliary Classifier GANs, while achieving performance comparable to or exceeding established AMP design methods. Additionally, cdGAN's flexible architecture allows for the simultaneous optimization of multiple peptide attributes. To demonstrate this capability, we introduce a multi-task classifier based on the Evolutionary Scale Modeling 2 (ESM2) model, enabling cdGAN to assess both antimicrobial activity and peptide structural properties in parallel. This enhancement improves the likelihood of generating viable therapeutic candidates with enhanced antimicrobial effectiveness and reduced toxicity.