生物制药
人工智能
计算机科学
机器学习
人工神经网络
强化学习
过程(计算)
制造工程
工程类
生物技术
生物
操作系统
作者
Anurag S. Rathore,Saxena Nikita,Garima Thakur,Somesh Mishra
标识
DOI:10.1016/j.tibtech.2022.08.007
摘要
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.
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