背景:血脑屏障(BBB)充当关键的结构屏障,并阻碍大多数神经治疗药物进入大脑。这对中枢神经系统(CNS)药物开发提出了重大挑战。因为缺乏有效的药物递送技术来克服这一障碍。BBB穿透肽(BBBP)有望克服BBB并促进药物分子向大脑的递送。因此,准确鉴定BBBP已成为CNS药物开发的关键步骤。然而,大多数计算方法是基于常规模型设计的,这些模型不足以捕获BBBP与BBB之间的复杂相互作用。此外,这些方法的性能进一步受到不平衡数据集的阻碍。
目的:本研究解决了BBBP预测中数据集不平衡的问题,并提出了有效准确识别BBBP的强大预测器,以及产生类似的BBBP。
方法:基于变压器的深度学习模型,DeepB3P,提出了预测BBBP的方法。反馈生成对抗网络(FBGAN)模型被用来有效地生成类似的BBBP,解决数据不平衡问题。
结果:FBGAN模型具有生成新型BBBP样肽的能力,有效缓解BBBP预测中的数据失衡问题。在基准数据集上进行的大量实验表明,DeepB3P优于其他BBBP预测模型约9.09%,特异性4.55%和9.41%,准确度,和马修的相关系数,分别。为了加快BBBP鉴定和CNS药物设计的进展,拟议的DeepB3P被实现为一个网络服务器,可以在http://cbcb访问。CDutcm.edu.cn/deepb3p/.
结论:DeepB3P提供的可解释分析提供了有价值的见解,并增强了BBBP识别的下游分析。此外,由FBGAN产生的BBBP样肽具有作为CNS药物开发候选物的潜力.
BACKGROUND: The blood-brain barrier (BBB) serves as a critical structural barrier and impedes the entry of most neurotherapeutic drugs into the brain. This poses substantial challenges for central nervous system (CNS) drug development, as there is a lack of efficient drug delivery technologies to overcome this obstacle. BBB penetrating peptides (BBBPs) hold promise in overcoming the BBB and facilitating the delivery of drug molecules to the brain. Therefore, precise identification of BBBPs has become a crucial step in CNS drug development. However, most computational methods are designed based on conventional models that inadequately capture the intricate interaction between BBBPs and the BBB. Moreover, the performance of these methods was further hampered by unbalanced datasets.
OBJECTIVE: This study addresses the problem of unbalanced datasets in BBBP prediction and proposes a powerful predictor for efficiently and accurately identifying BBBPs, as well as generating analogous BBBPs.
METHODS: A transformer-based deep learning model, DeepB3P, was proposed for predicting BBBP. The feedback generative adversarial network (FBGAN) model was employed to effectively generate analogous BBBPs, addressing data imbalance.
RESULTS: The FBGAN model possesses the ability to generate novel BBBP-like peptides, effectively mitigating the data imbalance in BBBP prediction. Extensive experiments on benchmarking datasets demonstrated that DeepB3P outperforms other BBBP prediction models by approximately 9.09%, 4.55% and 9.41% in terms of specificity, accuracy, and Matthew\'s correlation coefficient, respectively. For accelerating the progress in BBBP identification and CNS drug design, the proposed DeepB3P was implemented as a webserver, which is accessible at http://cbcb.cdutcm.edu.cn/deepb3p/.
CONCLUSIONS: The interpretable analyses provided by DeepB3P offer valuable insights and enhance downstream analyses for BBBP identification. Moreover, the BBBP-like peptides generated by FBGAN hold potential as candidates for CNS drug development.