Digital Pharmaceutical Sciences
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Review Article Digital Pharmaceutical Sciences Safa A. Damiati1,2
Received 7 April 2020; accepted 6 July 2020 Abstract.
Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.
KEY WORDS: artificial intelligence; machine learning; artificial neural networks; pharmaceutical sciences; pharmaceutical industry.
INTRODUCTION: BIG DATA IN PHARMACEUTICAL SCIENCES There has been a remarkable increase in the amount of data—including pharmaceutical data—that are generated each day. The term “big data” has gained increasing interest in various research areas. In addition, data-driven companies currently show how various industries are able to profit from the massive generation of data. Several definitions have been proposed for the term “big data.” One of the widely recognized definitions used is the “4 Vs” definition. The definition was first proposed by Douglas Laney and encompasses “3 Vs” which consist of volume, velocity, and variety (1,2). This definition was later extended by IBM to include the fourth “V” for veracity (3). However, the reported definitions of “big data” usually lack consistency and quantification. Because of its potential value, data has been considered as the new oil (4,5). Textbooks and publications, social media, user-generated content, electronic health records, genomics, sensor networks, and many other types of data all form “big data” and contribute to its diversity and complexity. The remarkable increase in the amount of data can be attributed to advancements in data storage and innovative technologies 1
Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University, P.O.Box 80260, Jeddah 21589, Saudi Arabia. 2 To whom correspondence should be addressed. (e–mail: [email protected])
(6). Almost 2.5 million new scientific papers are published annually (7). In addition, there were more than 15,000 PubMed-reported publications on
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