Prediction and Effect Analysis of Antifungal Peptides Based on Autoencoders and Convolutional Autoencoders

Abstract Fungal infections pose a growing global health threat exacerbated by the limited efficacy and rising antimicrobial resistance of conventional antifungal agents. Antifungal peptides (AFPs) emerge as promising alternatives due to their multimodal mechanisms of action and favorable toxicity profiles. To address the resource-intensive nature of traditional experimental screening, we present a multimodal deep learning framework that synergistically integrates autoencoder (AE) and convolutional autoencoder (CAE) architectures by leveraging one-hot encoding, multiple sequence information. Our innovative approach combines reconstruction losses from both AE and CAE with classification loss to optimize feature representation and enhance generalization capabilities. Rigorous evaluation on our independent test dataset, Antifp_Main, Antifp_DS1, Antifp_DS2 demonstrated superior performance with average accuracy of 91.71% and Matthews correlation coefficient (MCC) of 0.8319, significantly outperforming existing state-of-the-art methods. The framework’s extension to predict minimum inhibitory concentrations (MICs) against Candida albicans achieved a mean squared error of 0.3897 through regression analysis. Critical mechanistic insights emerged from feature importance analysis, identifying lysine and methionine as essential residues while revealing functional hotspots at positions 3 and 7 within peptide sequences. These findings not only validate predictive power of our model, but also provide actionable guidance for rational design of next-generation antifungal therapeutics.