AntiCancer Peptides (ACPs) have emerged as promising therapeutic agents for cancer treatment. The time‐consuming and costly nature of wet‐lab discriminatory methods has spurred the development of various machine learning and deep learning‐based ACP classification methods. Nonetheless, current methods encountered challenges in efficiently integrating features from various peptide modalities, thereby limiting a more comprehensive understanding of ACPs and further restricting the improvement of prediction model performance. In this study, we introduce a novel ACP prediction method, MA‐PEP, which leverages multiple attention mechanisms for feature enhancement and fusion to improve ACP prediction. By integrating the enhanced molecular‐level chemical features and sequence information of peptides, MA‐PEP demonstrates superior prediction performance across several benchmark datasets, highlighting its efficacy in ACP prediction. Moreover, the visual analysis and case studies further demonstrate MA‐PEP's reliable feature extraction capability and its promise in the realm of ACP exploration. The code and datasets for MA‐PEP are available at https://github.com/liangxiaodata/MA-PEP.

中文翻译：

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MA-PEP：一种基于注意力机制的多模态特征融合的新型抗癌肽预测框架

抗癌肽（ACP）已成为治疗癌症的有前途的治疗剂。湿实验室判别方法耗时且成本高昂，刺激了各种机器学习和基于深度学习的 ACP 分类方法的发展。尽管如此，当前的方法在有效整合各种肽模态的特征方面遇到了挑战，从而限制了对 ACP 的更全面的理解，并进一步限制了预测模型性能的提高。在本研究中，我们引入了一种新颖的 ACP 预测方法 MA-PEP，该方法利用多种注意力机制进行特征增强和融合来改进 ACP 预测。通过整合增强的分子级化学特征和肽序列信息，MA-PEP 在多个基准数据集上展示了卓越的预测性能，突出了其在 ACP 预测中的功效。此外，视觉分析和案例研究进一步证明了 MA-PEP 可靠的特征提取能力及其在 ACP 勘探领域的前景。 MA-PEP 的代码和数据集可在以下位置获取：https://github.com/liangxiaodata/MA-PEP。