Supporting Data for manuscript entitled "sAMP-VGG16: A Drude Polarizable Force Field assisted Deep Transfer Learning based Prediction Model for Short Antimicrobial Peptides"

During the last three decades, antimicrobial peptides (AMPs) have emerged as a promising therapeutic alternative to antibiotics. The approaches for designing AMPs span from experimental trial-and-error methods to synthetic hybrid peptide libraries. To overcome the exceedingly expensive and time-consuming process of designing effective AMPs, many computational and machine-learning tools for AMP prediction have been recently developed. In general, to encode the peptide sequences, featurization in these rely on approaches based on (a) amino acid composition, (b) physicochemical properties, (c) sequence similarity, and (d) structural properties. In this work, we present an image-based deep neural network model to predict AMPs, where we are using feature encoding based on Drude polarizable force-field atom types, which can capture the peptide properties more efficiently compared to conventional feature vectors. The proposed prediction model identifies AMPs with promising accuracy and efficiency and can be used as a next-generation screening method for predicting new AMPs.