ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY MARKET ANALYSIS

Market Challenges And Opportunities

Global Artificial Intelligence in Drug Discovery Market Drivers:

• Rising prevalence of chronic diseases supporting development of personalized drugs to propel market growth: The increase in prevalence of chronic diseases is one of the key factors that has raised the need to understand and diagnose diseases in their early stages. With AI-based deep learning technologies, it would become easy to predict diseases based on their historic health data and simplify the drug discovery process related to a particular condition. Thus, many healthcare firms have started collaborating with AI-based companies for the development of a particular drug, effective for a chronic condition, with the use of AI. For instance, in 2019, GlaxoSmithKline partnered with Exscientia, an AI-driven drug development and discovery company, for development of drug targeted novel treatment for Chronic Obstructive Pulmonary Disease (COPD). Such instances signify growth potential and rising adoption of AI-based drug discovery processes for chronic conditions.
• Rising need to curb drug development and discovery costs to drive market growth: Target identification and validation for drug development is a crucial step for the overall drug discovery process. Increasing application of AI in target identification is another factor propelling the adoption of AI in drug discovery process. In addition, many key players in healthcare industry are trying to adopt AI-based processes to curb costs of drug development. For instance, according to an article published by Reuters, in 2017, AstraZeneca and Berg collaborated for identification of new drug targets with the help of AI-based analysis of tissue samples. Moreover, key players such as Pfizer, Takeda, Roche, and Sanofi are trying to gain foothold in emerging AI-based drug discovery market. For instance, according to a report published by Deloitte, as of 2019, Pfizer disclosed around five deals involving AI. Hence, increasing importance of AI in overall healthcare sector is likely to increase its adoption in drug development process, thereby reducing the total cost of drug discovery and development.

Global Artificial Intelligence in Drug Discovery Market Opportunities:

• Developed countries, such as the U.S., Australia, Canada, and the UK, spend a large proportion of their GDP on healthcare. In these economies, the cost and demand for healthcare is growing rapidly, thus increasing the need for digital technologies, such as AI. In recent years, the U.S. and the UK have adopted AI technologies to reduce the cost of care and improve clinical services. In the U.S., AI has been primarily adopted because of the shift from traditional to value-based healthcare systems. For instance, in 2017, NHS organized chatbots, based on AI, on trial basis to reduce the pressure on emergency triage process.

Global Artificial Intelligence in Drug Discovery Market Restraints:

• Inadequate availability and quality of data to hinder market growth: Large pharmaceutical and healthcare companies are often concerned about the utilization of large amount of data that gets created due to distinct experimental platforms. In addition, the utilization of this data, in a consolidated form, for analytical purposes and drug discovery is many times not considered by firms. This impedes the growth of AI in drug discovery process
• Lack of digital infrastructure in emerging markets to hamper the market growth: Maintaining Electronic Health Records (EHRs) & healthcare claims data and providing telemedicine services & patient data analytics require a well-established digital infrastructure. Currently, digital health services are being used only on end-to-end basis in developed economies. On the contrary, lack of well-developed digital health infrastructure in various emerging and underdeveloped economies is a key factor that may limit market growth. To establish AI systems, there is a need of high-end infrastructure. Lack of financial resources makes it difficult for stakeholders in emerging economies to adopt these systems effectively.