Kavousipour Soudabeh, Barazesh Mahdi, Mohammadi Shiva
Molecular Medicine Research Center, Hormozgan Health Institute, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.
Department of Medical Biotechnology, Cellular and Molecular Research Center, Gerash University of Medical Sciences, Gerash, Iran.
Med Biol Eng Comput. 2025 Sep 1. doi: 10.1007/s11517-025-03429-4.
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.
抗体是制药领域的一类关键治疗药物，能够精准靶向致病因子。传统的抗体设计方法缓慢、成本高且有局限性。高通量数据以及包括机器学习、深度学习和强化学习在内的人工智能（AI）的进展，彻底改变了抗体序列设计、三维结构预测以及亲和力和特异性的优化。计算方法能够实现快速文库生成和高效筛选，减少实验采样，并支持具有改善免疫反应的合理设计。将人工智能与实验方法相结合可实现全新的多功能抗体开发。人工智能还通过分析大型数据集、预测相互作用以及指导修饰以提高疗效和安全性，加速了发现过程、靶点识别和候选物优先级排序。尽管存在挑战，但正在进行的研究不断拓展人工智能的潜力，并改变抗体开发及制药行业。抗体是制药领域的一类关键治疗药物，能够精准靶向致病因子。传统的抗体设计方法缓慢、成本高且有局限性。高通量数据以及包括机器学习、深度学习和强化学习在内的人工智能（AI）的进展，彻底改变了抗体序列设计、三维结构预测以及亲和力和特异性的优化。计算方法能够实现快速文库生成和高效筛选，减少实验采样，并支持具有改善免疫反应的合理设计。将人工智能与实验方法相结合可实现全新的多功能抗体开发。人工智能还通过分析大型数据集、预测相互作用以及指导修饰以提高疗效和安全性，加速了发现过程、靶点识别和候选物优先级排序。尽管存在挑战，但正在进行的研究不断拓展人工智能的潜力，并改变抗体开发及制药行业。
