Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

Autor: Doo Nam Kim, Andrew D. McNaughton, Neeraj Kumar
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Bioengineering, Vol 11, Iss 2, p 185 (2024)
Druh dokumentu: article
ISSN: 2306-5354
DOI: 10.3390/bioengineering11020185
Popis: This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.
Databáze: Directory of Open Access Journals
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