Artificial Intelligence (AI) In Drug Discovery Global Market Report 2020-30: COVID-19 Growth and Change

New York, Dec. 09, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Artificial Intelligence (AI) In Drug Discovery Global Market Report 2020-30: COVID-19 Growth and Change" - https://www.reportlinker.com/p05995086/?utm_source=GNW


The global artificial intelligence (AI) in drug discovery market is expected to decline from $0.37 billion in 2019 to $0.36 million in 2020 at a compound annual growth rate (CAGR) of -1.33%. The decline is mainly due to the COVID-19 outbreak that has led to restrictive containment measures involving social distancing, remote working, and the closure of industries and other commercial activities resulting in operational challenges. The entire supply chain has been disrupted, impacting the market negatively. The market is then expected to recover and reach $1.1 billion in 2023 at a CAGR of 42.9%.

The artificial intelligence (AI) in drug discovery market consists of sales of AI for drug discovery and related services. Artificial Intelligence (AI) for drug discovery is a technology that uses a simulation of human intelligence process by machines to tackle complex problems in the drug discovery process. It helps to find new molecules to identify drug targets and develop personalized medicines in the pharmaceutical industry.

North America was the largest region in artificial intelligence (AI) in the drug discovery market in 2019.

In July 2020, Curi Bio, a leading developer of human iPSC-based platforms for drug discovery, acquired Dana Solutions for an undisclosed amount. This acquisition will give Curi Bio access to Dana’s AI/ML platforms for modeling cell and tissue phenotype, automated platform for contractility analysis of beating cardiomyocyte and platform for predictive safety pharmacology. Dana Solutions is a leading AI solutions provider for the life sciences industry.

The artificial intelligence (AI) in drug discovery market covered in this report is segmented by technology into deep learning; machine learning. It is also segmented by drug type into small molecule; large molecules, by therapeutic type into metabolic disease; cardiovascular disease; oncology; neurodegenerative diseases; others and by end-users into pharmaceutical companies; biopharmaceutical companies; academic and research institutes; others.

The shortage of skilled professionals is expected to hamper the AI in drug discovery market. The employees have to re-train or learn new skill sets to work efficiently on the complex AI machines to get the desired results for the drug. The shortage of skills acts as a major hindrance to drug discovery through AI, discouraging companies to adopt AI-based machines for drug discovery. According to The Pistoia Alliance, a global non-profit organization that works to encourage innovation in life sciences R&D cited that 44% of respondents agree that lack of skills was the biggest barrier to the adoption of AI and machine learning (ML) in 2019.

Major companies operating in the AI drug delivery market are adopting strategic collaborations to research and discover drugs using AI and are gaining significant popularity in the artificial intelligence in drug discovery market. For instance, in September 2019, Atomwise Inc. and Jiangsu Hansoh Pharmaceutical Group Co. Ltd announced the collaboration of $1.5 billion to design and discover the potential drug candidates for up to 11 undisclosed target proteins in cancer and other therapeutic areas. The collaboration is expected to combine Atomwise Inc’s AI technology, protein structure expertise, and medicinal chemistry with Jiangsu Hansoh Pharmaceutical Group Co. Ltd’s manufacturing, research & development, and commercial capabilities in order to improve and accelerate clinical development and drug discovery process.

The rise in demand for a reduction in the overall time taken for the drug discovery process is a key driver propelling the growth of the artificial intelligence (AI) in drug discovery market. Traditionally, it takes three to five years for animal models to identify and optimize molecules before they are evaluated in humans whereas start-ups based on AI have been identifying and designing new drugs in a matter of few days or months. For instance, in 2020, the British startup Exscientia and Japan’s Sumitomo Dainippon Pharma have used artificial intelligence to produce an obsessive-compulsive disorder (OCD) medication, decreasing the development time from four years to less than one year. The reduction in overall time taken for the drug discovery process drives the artificial intelligence (AI) in drug discovery market’s growth.




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