Deep Learning in Drug Discovery and Deep Learning in Diagnostics Market is anticipated to grow at over 20% CAGR, claims Roots Analysis

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May 02, 2023, 10:30 ET

Given the potential of deep learning-based technologies to expedite the drug discovery process and improve early-stage diagnosis, the deep learning domain is anticipated to be one of the most valuable AI segments in the healthcare industry

LONDON, May 2, 2023 /PRNewswire/ -- Roots Analysis has announced the addition of "Deep Learning in Drug Discovery Market and Deep Learning in Diagnostics Market (2nd Edition), 2023-2035" report to its list of offerings.

The traditional statistical tools and techniques available for medical data interpretation are known to be associated with challenges related to time and cost. This has prompted industry stakeholders to explore the applications of deep learning-based technologies, specifically across drug discovery and diagnosis. In addition, recent investments made in this domain are likely to offer lucrative opportunities to companies offering deep learning based technologies / solutions within the healthcare industry.

To order this 320+ page report, which features 158+ figures and 146+ tables, please visit https://www.rootsanalysis.com/reports/view_document/deep-learning-in-drug-discovery-market/156.html

Key Market Insights

Currently, more than 70 players claim to offer deep learning-powered services / technologies for drug discovery

The current market landscape is dominated by the presence of small players, which constitute 57% of the total number of stakeholders. It is worth mentioning that, amongst these, over 47% of the players are based in North America. In addition, more than 130 players offer deep learning technologies for diagnostic applications.

Over 35 start-ups focused on deep learning in diagnostics have emerged in the last 8 years

Of these, more than 40% of the players are based in Europe. This is followed by players headquartered in North America, accounting for around 34% of the start-ups focused on deep learning in drug discovery. Further, over 55% of the start-ups engaged in this domain primarily focus on medical diagnosis.

More than 425 clinical trials evaluating deep learning technologies have been registered till date, worldwide

The clinical activity (in terms of the number of trials registered) increased at a CAGR of 40% during the period 2018-2022. Of the total number of trials, close to 65% are presently active. Among the active trials, 70% are currently recruiting patients.

Close to USD 15 billion has been invested by both private and public investors across 200+ instances

It is important to mention that, between 2019 and 2022, majority of the amount was raised through venture capital rounds (62%) and secondary offerings (16%). Further, more than 55% of the funding instances were reported by players based in North America. Overall, more than 215 investors have financed various projects / initiatives focused on deep learning.

The use of deep learning-based solutions in drug discovery has demonstrated the ability to enable up to 20% cost savings

Based on inputs gathered from both secondary and primary sources, the report features an informed and insightful section estimating the cost-saving potential associated with deep learning-based solutions in drug discovery and diagnostics. In fact, the adoption of such technologies has the potential to save more than USD 22.3 billion in diagnostics by 2035.

North America and Asia Pacific are anticipated to capture over 77% of the deep learning in drug discovery market by 2035

In addition, the market in Europe is likely to grow at a relatively faster pace (CAGR of 23.7%) in the long term. Further, in 2035, deep learning in the drug discovery market for oncological disorders is expected to capture the majority share (36%) of the total market.

Deep learning in diagnostics market is expected to grow at a CAGR of 20% by 2035

In terms of therapeutic area, majority of the revenues (34%) are likely to come in from deep learning technologies being used for the diagnosis of oncological disorders. On the other hand, the deep learning market for diagnosis of musculoskeletal disorders is expected to grow at a faster pace (CAGR of 22.9%) during the forecasted period.

To request a sample copy / brochure of this report, please visit https://www.rootsanalysis.com/reports/156/request-sample.html

Key Questions Answered

What is deep learning? What are the major factors driving the application of deep learning in drug discovery and diagnostics market?
Which companies offer deep learning technologies / services for drug discovery and diagnostics?
What is the trend of funding and investment in the field of deep learning in drug discovery and diagnostics?
How many clinical trials evaluating deep learning technologies are being conducted globally?
What is the likely cost-saving potential associated with the use of deep learning-based technologies in diagnostics?
Which therapeutic area accounts for the largest share in deep learning market in drug discovery?
Which region is expected to witness the highest growth rate in the deep learning market for diagnostics?

The financial opportunity associated with the deep learning in drug discovery and deep learning in diagnostics market has been analyzed across the following segments:

Therapeutic Area
Oncological Disorders
Infectious Diseases
Neurological Disorders
Immunological Disorders
Endocrine Disorders
Cardiovascular Disorders
Respiratory Disorders
Ophthalmic Disorders
Musculoskeletal Disorders
Inflammatory Disorders
Other Disorders
Key Geographical Regions
North America
Europe
Asia Pacific
Rest of the World

The opinions and insights presented in the report were also influenced by discussions held with eminent stakeholders in the industry. The report features detailed transcripts of interviews held with the following industry stakeholders:

Avi Veidman (Chief Executive Officer, Nucleai), Yoav Blum (Director of AI, Nucleai) and Ken Bloom (Head of Pathology, Nucleai)
Kevin Choi (Chief Executive Officer, Mediwhale)
Babak Rasolzadeh (Former Vice President, Product and Software Development, Arterys)
Vikas Karade (Founder, Chief Executive Officer, AlgoSurg)
Walter de Back (Former Research Scientist, Context Vision)
Mausumi Acharya (Chief Executive Officer, Advenio Technosys)
Carla Leibowitz (Former Head, Strategy and Marketing, Arterys)
Deekshith Marla (Founder, Chief Technology Officer, Arya.ai) and Sanjay Bhadra (Chief Business Officer, Arya.ai)

The research also includes profiles of key players (listed below) employing deep learning solutions for drug discovery and diagnostics purposes; each profile features a brief overview of the company, its financial information, service portfolio, recent developments and an informed future outlook.