Deep Learning Combined with Quantitative Structure‒Activity Relationship Accelerates De Novo Design of Antifungal Peptides

Abstract

Novel antifungal drugs that evade resistance are urgently needed for Candida infections. Antifungal peptides (AFPs) are potential candidates due to their specific mechanism of action, which makes them less prone to developing drug resistance. An AFP de novo design method, Deep Learning-Quantitative Structure‒Activity Relationship Empirical Screening (DL-QSARES), is developed by integrating deep learning and quantitative structure‒activity relationship empirical screening. After generating candidate AFPs (c_AFPs) through the recombination of dominant amino acids and dipeptide compositions, natural language processing models are utilized and quantitative structure‒activity relationship (QSAR) approaches based on physicochemical properties to screen for promising c_AFPs. Forty-nine promising c_AFPs are screened, and their minimum inhibitory concentrations (MICs) against C. albicans are determined to be 3.9–125 µg mL−1, of which four leading c_AFPs (AFP-8, −10, −11, and −13) has MICs of <10 µg mL−1 against the four tested pathogenic fungi, and AFP-13 has excellent therapeutic efficacy in the animal model.