Peptide is a promising candidate for therapeutic and diagnostic development due to its great physiological versatility and structural simplicity. Thus, identifying therapeutic peptides and investigating their properties are fundamentally important. As an inexpensive and fast approach, machine learning-based predictors have shown their strength in therapeutic peptide identification due to excellences in massive data processing. To date, no reported therapeutic peptide predictor can perform high-quality generic prediction and informative physicochemical properties (IPPs) identification simultaneously.

中文翻译：

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PPTPP：一种使用理化性质编码和自适应特征表示学习的新型治疗性肽预测方法。

由于其巨大的生理多功能性和结构简单性，该肽是治疗和诊断开发的有希望的候选者。因此，鉴定治疗性肽并研究其性质至关重要。作为一种廉价且快速的方法，基于机器学习的预测器由于在海量数据处理方面的卓越表现，已显示出其在治疗性肽段识别中的优势。迄今为止，尚无报道的治疗性肽预测因子可以同时进行高质量的通用预测和信息性理化特性（IPP）鉴定。