Open AccessInvestigating the Impact of Machine Learning in Pharmaceutical Industry
Author(s) -
S. Nagaprasad,
D. Lakshmi Padmaja,
Yaser Qureshi,
Sunil L. Bangare,
Manmohan Mishra,
Bireshwar Dass Mazumdar
Publication year - 2021
Publication title -
journal of pharmaceutical research international
Language(s) - English
Resource type - Journals
ISSN - 2456-9119
DOI - 10.9734/jpri/2021/v33i46a32834
Subject(s) - machine learning , artificial intelligence , pharmaceutical industry , artificial neural network , identification (biology) , computer science , medicine , botany , pharmacology , biology
In the pharmaceutical and consumer health industries, artificial intelligence and machine learning played an important role. These technologies are critical for the identification of patients with improved intelligence applications, such as disease detection and diagnostics for clinical testing, for medicine production and predictive forecasts. In recent years, advances in numerous analysis tools and machine learning algorithms have led to novel applications for machine learning in several areas of pharmaceutical science. This paper examines the past, present, and future impacts of machine learning on several areas, including medicine design and discovery. Artificial neural networks are employed in pharmaceutical machine learning because they can reproduce nonlinear interactions typical in pharmaceutical research. AI and learning machines are examined in everyday pharmaceutical needs, industrial and regulatory insights.