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 relies on approaches based on (a) amino acid (AA) 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 short AMPs (≤30 AA) with promising accuracy and efficiency and can be used as a next‐generation screening method for predicting new AMPs. The source code is publicly available at the Figshare server sAMP‐VGG16.

中文翻译：

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sAMP-VGG16：基于力场辅助图像的短抗菌肽深度神经网络预测模型

在过去的三十年中，抗菌肽（AMP）已成为抗生素的一种有前景的治疗替代品。设计 AMP 的方法涵盖从实验试错法到合成混合肽库。为了克服设计有效 AMP 的极其昂贵和耗时的过程，最近开发了许多用于 AMP 预测的计算和机器学习工具。一般来说，为了编码肽序列，特征化依赖于基于 (a) 氨基酸 (AA) 组成、(b) 物理化学特性、(c) 序列相似性和 (d) 结构特性的方法。在这项工作中，我们提出了一种基于图像的深度神经网络模型来预测 AMP，其中我们使用基于 Drude 极化力场原子类型的特征编码，与传统特征向量相比，它可以更有效地捕获肽属性。所提出的预测模型可识别短 AMP（≤30 AA），具有良好的准确性和效率，可用作预测新 AMP 的下一代筛选方法。源代码可在 Figshare 服务器 sAMP-VGG16 上公开获取。