Advances of deep Neural Networks (DNNs) in the development of peptide drugs

Peptides are able to bind to difficult disease targets with high potency and specificity, providing great opportunities to meet unmet medical requirements. Nevertheless, the unique features of peptides, such as their small size, high structural flexibility, and scarce data availability, bring extra challenges to the design process. Firstly, this review sums up the application of peptide drugs in treating diseases. Then, the review probes into the advantages of Deep Neural Networks (DNNs) in predicting and designing peptide structures. DNNs have demonstrated remarkable capabilities in structural prediction, enabling accurate three-dimensional modeling of peptide drugs through models like AlphaFold and its successors. Finally, the review deliberates on the challenges and coping strategies of DNNs in the development of peptide drugs, along with future research directions. Future research directions focus on further improving the accuracy and efficiency of DNN-based peptide drug design, exploring novel applications of peptide drugs, and accelerating their clinical translation. With continuous advancements in technology and data accumulation, DNNs are poised to play an increasingly crucial role in the field of peptide drug development.

多肽能够以高结合效力与特异性结合难治性疾病靶点，为满足未被满足的医疗需求提供了重要机遇。然而，多肽的独特性质，如分子量小、结构灵活性高、可用数据稀缺，给其设计过程带来了额外挑战。首先，本综述总结了多肽药物在疾病治疗中的应用。随后，本文探讨了深度神经网络（Deep Neural Networks, DNNs）在多肽结构预测与设计中的优势。深度神经网络已在结构预测领域展现出卓越性能，可通过AlphaFold及其后续模型实现多肽药物的精准三维建模。最后，本综述深入讨论了深度神经网络在多肽药物开发中面临的挑战与应对策略，以及未来研究方向。未来研究方向聚焦于进一步提升基于深度神经网络的多肽药物设计的准确性与效率、探索多肽药物的全新应用场景，以及加速其临床转化。随着技术的持续进步与数据积累，深度神经网络有望在多肽药物开发领域发挥愈发关键的作用。