## 1 Introduction  
Medical bioinformatics challenges problems of pharmaceutical product optimization based on Artificial Intelligence (AI) approaches. The lifecycle of a pharmaceutical product begins with Research and Development. In the first stage, it is necessary to search for the composition of a medicinal product and determine its biological activity based on the properties of its components, as well as the technology of future production. The development phase is usually one of the most expensive and time-consuming in bringing a new drug to market. This is followed by pre-clinical studies of drugs across in vitro and in vivo models. After passing the previous stage, an application is submitted for conducting clinical trials. At this stage of the drug product life cycle, a trial design is created, and after its approval, clinical trials are carried out. This is followed by the market authorization of the medicinal product. The developer of the medicinal product sends for examination the dossier materials containing the information collected during the previous stages and pharmaceutical product samples. The organization of production is usually carried out in parallel with the previous stages. Production issues include not only the presentation of the active substance in a consumer available dosage form but also the selection of the necessary auxiliary substances. The fundamental aspect of this system is quality assurance at all stages of production--from the purchase and quality control of the raw materials to the final product quality control.  
The next stage is storage and transportation provided by the manufacturer or by the distributor. During wholesale, the possibility of contamination is excluded, and the observance of the cold chain is ensured. This process also capacitates the delivery of drugs to medical and pharmaceutical facilities at the right time. Retail sale follows after wholesale. Medical use is the main goal of drug development and the main stage of their circulation. The use of medicines is carried out as prescribed by medical workers or independently by patients (responsible self-medication with Over-the-Counter (OTC) drugs). Unfortunately, the main effect of drugs may be accompanied by side effects (any other types of action, except for the main one, including adverse reactions and post-vaccination complications) or the lack of declared effectiveness. The medical use of drugs must be simultaneously accompanied by the monitoring of their safety and effectiveness.  
The use of AI can be considered for each of the listed lifecycle stages. AI in pharmaceutical companies refers to the use of automated algorithms to perform multiple tasks which traditionally rely on human intelligence. Over the last years, the use of AI in the pharmaceutical and biotechnology industry has redefined how scientists identify new drug targets, perform drug repositioning and repurposing, generate novel molecules, conduct clinical trials, and perform all other activities across the medical product lifecycle.  
One of the main reasons for the interest in AI is cost reduction for drug development. A study published by the Massachusetts Institute of Technology found that only 13.8% of drugs successfully pass clinical trials [1]. A company can expect to pay USD 1.3 billion on average for any drug to complete the entire clinical trial process and obtain Food and Drug Administration (FDA) approval [2].  
The aim of our work was to systematically study the general approach to the introduction of AI in the life cycle of pharmaceutical products. In the next sections, we review AI approaches in pharmaceutical product studies, methods of the studies search, results of the search (existing AI systems), and discuss the applications, such as drug repositioning.