Artificial intelligence in antibody design and development: harnessing the power of computational approaches.

IF 2.6 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Soudabeh Kavousipour, Mahdi Barazesh, Shiva Mohammadi
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引用次数: 0

Abstract

Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry. Antibodies are a key therapeutic class in pharma, enabling precise targeting of disease agents. Traditional methods for their design are slow, costly, and limited. Advances in high-throughput data and artificial intelligence (AI) including machine learning, deep learning, and reinforcement learning have revolutionized antibody sequence design, 3D structure prediction, and optimization of affinity and specificity. Computational approaches enable rapid library generation and efficient screening, reduce experimental sampling, and support rational design with improved immune response. Combining AI with experimental methods allows for de novo, multifunctional antibody development. AI also accelerates the discovery process, target identification, and candidate prioritization by analyzing large datasets, predicting interactions, and guiding modifications to enhance efficacy and safety. Despite challenges, ongoing research continues to expand the potential of AI and transform antibody development and the pharmaceutical industry.

抗体设计和开发中的人工智能:利用计算方法的力量。
抗体是制药领域的关键治疗类别,能够精确靶向疾病药物。传统的设计方法是缓慢、昂贵和有限的。高通量数据和人工智能(AI)的进步,包括机器学习、深度学习和强化学习,已经彻底改变了抗体序列设计、3D结构预测以及亲和力和特异性的优化。计算方法能够快速生成文库和有效筛选,减少实验采样,并支持合理设计,提高免疫反应。将人工智能与实验方法相结合,可以重新开发多功能抗体。人工智能还通过分析大型数据集、预测相互作用和指导修改以提高疗效和安全性,加速了发现过程、目标识别和候选优先级的确定。尽管面临挑战,正在进行的研究仍在继续扩大人工智能的潜力,并改变抗体开发和制药行业。抗体是制药领域的关键治疗类别,能够精确靶向疾病药物。传统的设计方法是缓慢、昂贵和有限的。高通量数据和人工智能(AI)的进步,包括机器学习、深度学习和强化学习,已经彻底改变了抗体序列设计、3D结构预测以及亲和力和特异性的优化。计算方法能够快速生成文库和有效筛选,减少实验采样,并支持合理设计,提高免疫反应。将人工智能与实验方法相结合,可以重新开发多功能抗体。人工智能还通过分析大型数据集、预测相互作用和指导修改以提高疗效和安全性,加速了发现过程、目标识别和候选优先级的确定。尽管面临挑战,正在进行的研究仍在继续扩大人工智能的潜力,并改变抗体开发和制药行业。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical & Biological Engineering & Computing
Medical & Biological Engineering & Computing 医学-工程:生物医学
CiteScore
6.00
自引率
3.10%
发文量
249
审稿时长
3.5 months
期刊介绍: Founded in 1963, Medical & Biological Engineering & Computing (MBEC) continues to serve the biomedical engineering community, covering the entire spectrum of biomedical and clinical engineering. The journal presents exciting and vital experimental and theoretical developments in biomedical science and technology, and reports on advances in computer-based methodologies in these multidisciplinary subjects. The journal also incorporates new and evolving technologies including cellular engineering and molecular imaging. MBEC publishes original research articles as well as reviews and technical notes. Its Rapid Communications category focuses on material of immediate value to the readership, while the Controversies section provides a forum to exchange views on selected issues, stimulating a vigorous and informed debate in this exciting and high profile field. MBEC is an official journal of the International Federation of Medical and Biological Engineering (IFMBE).
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