化学
灵活性(工程)
深度学习
药物开发
肽
药物发现
领域(数学)
数据科学
计算生物学
药品
人工智能
药理学
生物化学
计算机科学
统计
生物
数学
纯数学
作者
Xinyi Wu,Huitian Lin,Renren Bai,Hongliang Duan
标识
DOI:10.1016/j.ejmech.2024.116262
摘要
Peptides can bind challenging disease targets with high affinity and specificity, offering enormous opportunities for addressing unmet medical needs. However, peptides' unique features, including smaller size, increased structural flexibility, and limited data availability, pose additional challenges to the design process compared to proteins. This review explores the dynamic field of peptide therapeutics, leveraging deep learning to enhance structure prediction and design. Our exploration encompasses various facets of peptide research, ranging from dataset curation handling to model development. As deep learning technologies become more refined, we channel our efforts into peptide structure prediction and design, aligning with the fundamental principles of structure-activity relationships in drug development. To guide researchers in harnessing the potential of deep learning to advance peptide drug development, our insights comprehensively explore current challenges and future directions of peptide therapeutics.
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