Disentangling the peptide space: a contrastive approach with wasserstein autoencoders

Abstract

Antimicrobial peptides (AMPs) have been shown to be promising therapeutic approaches against antibiotic-resistant pathogens. In the ongoing search for new AMPs, data-driven methods, especially generative models, have become indispensable tools for expediting discovery. We introduce a novel architecture, Contrastive Wasserstein Autoencoder (C-WAE), designed for the de novo generation of AMP candidates by establishing a discriminative latent space of amino acid sequences. The architecture combines Wasserstein distance metrics with a contrastive loss function to achieve a highly separable latent space where AMPs and non-AMPs are distinctly classified. Further, a predictive models trained on a separate validation set could correctly classify as antimicrobial >90% of samples. Empirical evaluations confirm that the C-WAE succeeds in generating high-quality candidate AMPs as predicted by classifier. Our contributions are twofold: 1) A new architecture for candidate AMP generation using contrastive learning, and 2) To the best of our understanding, this is the first study that integrates contrastive learning for the de novo synthesis of AMPs.