Current Trends of Artificial Intelligence in Biotechnology

Artificial intelligence (AI) is already widely used in biotechnology to solve a variety of problems. These include, for example, drug discovery, drug safety, functional and structural proteomics/genomics, metabolomics, pharmacology, pharmacogenetics and pharmacogenomics, among many others. Future advances in this domain depend critically on the ability of biotechnology researchers to use advanced AI solutions effectively. The biotechnology industry currently relies heavily on data storage, filtering, analysis and sharing. Biotechnology companies and various healthcare organizations around the world already maintain huge data bases. Drug manufacturing, chemical analysis of various compounds, sequencing of RNA and DNA, enzyme studies, and other similar biological processes all require strong support from AI software solution to move faster and reduce manual errors. It is important to emphasize at the very beginning that all the successful AI we are describing today relies entirely on digital technology to function. Digitalization is therefore the very first step towards any AI application. In many cases, AI systems are integrated with other digital technologies such as sensors, actors (cyber-physical systems (CPS), often just called robots), and technology to enable the automation of tasks and the collection and analysis of data. Overall, the development and use of AI is dependent on digital technology - the basis for it is digital computers. Digital transformation refers to the use of digital technologies to fundamentally change the way companies, organizations, research institutions and universities operate. In the context of biotechnology, digital transformation can involve the introduction of new technologies and processes to improve the efficiency, accuracy, and speed of research and development and enable the development of entirely new and disruptive products and services. Digital transformation can help accelerate the development and use of AI in biotechnology by providing access to big data and automating certain tasks, which can help improve the efficiency and accuracy of research and development. In this Editorial, it has been clearly stated what exactly AI means, concomitant with explaining the specific differences between AI, machine learning, and deep learning to provide a worthy common understanding. Afterwards, there is successful introduction of significant domains of biotechnology where AI is being applied or may be applicable in the future. Thereafter, this editorial presents certain cross-cutting challenges where it is predominantly significant to advance future studies