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Pharmaceutical Fronts
Article . 2025 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Pharmaceutical Fronts
Article . 2025
Data sources: DOAJ
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Application of Artificial Intelligence and Computational Biology in Protein Drug Development

Authors: Jiacheng Jiang; Wen Li; Weiran Huang; Xinyi Lou; Xinyi Shi; Chen Guo; Xueni Yu; +6 Authors

Application of Artificial Intelligence and Computational Biology in Protein Drug Development

Abstract

AbstractProtein drugs have evolved into a primary category of biological drugs. Despite the impressive achievements, protein therapeutics still face several challenges, including potential immunogenicity, druggability, and high costs. In recent years, artificial intelligence (AI) and computational biology have emerged as powerful tools to overcome these challenges and reshape the protein drug development pipeline. This review underscores the pivotal role of AI in advancing protein drug development, including the computational analysis of phage libraries, the application of computer-aided techniques for new phage display systems, and the computational optimization and design of novel antibody–drug conjugates, nanobodies, and cytokines. The review delves into the use of AI in predicting the pharmacological properties of these protein therapeutics, providing a comprehensive overview of the transformative impact of computational approaches in these areas.

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Keywords

RS1-441, computational biology, Pharmacy and materia medica, phage display, artificial intelligence, nanobodies, cytokines, protein drug

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
gold
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