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Deep Learning Solutions in Drug Discovery and Diagnostics are Likely to Provide Cost and Time Saving Opportunities


Published : Apr 10, 2017

Roots Analysis has announced the addition of “Deep Learning in Drug Discovery and Diagnostics, 2017-2035” report to its list of offerings. The report provides a comprehensive analysis of the current landscape and future outlook of the growing market of deep learning solutions within the healthcare domain.

 

Rohit Kumar, the principal analyst stated that, “Various deep learning solutions are currently available / being developed to cater to unmet medical needs and generate relevant insights from medical data. These solutions are anticipated to open up significant opportunities in the field of drug discovery and diagnostics as the healthcare industry gradually shifts towards digital solutions.”

 

One of the primary objectives of the study was to project the growth of this market segment and evaluate the future prospects of deep learning within the healthcare industry. Amongst other things, the study covers the following:

  • The current status of the market with respect to key players, specific applications and the therapeutic areas in which these solutions can be applied.
  • Various initiatives that are being undertaken by technology giants, such as IBM, Google, Facebook, Microsoft, Nvidia and Samsung. The presence of these stakeholders signifies the opportunity and the impact that these solutions are likely to have in the near future. Specifically, we have presented a comparative analysis of the deep learning solutions developed by IBM and Google.
  • Detailed profiles of some of the established, as well as emerging players in the industry, highlighting key technology features, primary applications and other relevant information.
  • The impact of venture capital funding in this area. It is important to mention that since the industry has witnessed the emergence of several start-ups, funding is a key enabler that is likely to drive both innovation and product development in the coming years.
  • An elaborate valuation analysis of companies that are involved in applying deep learning in drug discovery and diagnostics. We built a multi-variable dependent valuation model to estimate the current valuation of a number of companies focused in this domain.
  • Future growth opportunities and likely impact of deep learning in the drug discovery and diagnostics domains. The forecast model, backed by robust secondary research and credible inputs from primary research, was primarily based on the likely time-saving and its associated cost-saving opportunity to the healthcare system.

 

Kumar further added, “The likely benefits associated with the use of deep learning based solutions in drug discovery and diagnostics are estimated to be worth multi billion dollars. There are well-known references where deep learning models have accelerated the drug discovery process and provided solutions to precision medicine. With potential applications in drug repurposing and preclinical research, deep learning in drug discovery is likely to offer immense opportunity. Similarly, in diagnostics, an increase in the speed of diagnosis is likely to have a profound impact in regions with large patient to physician ratios. The implementation of such solutions is anticipated to increase the efficiency of physicians, providing a certain amount of relief to the overly-burdened global healthcare system.”

 

The report highlights the contributions of several players in the field; some of the examples are listed below:

  • 23andMe
  • Arterys
  • Atomwise
  • Bay Labs
  • Benevolent.ai
  • BERG Health
  • Butterfly Network
  • Cloud Pharmaceuticals
  • Deep Genomics
  • Desktop Genetics
  • Enlitic
  • Flatiron Health
  • Google
  • Human Longevity
  • Hummingbird Bioscience
  • IBM
  • iCarbonX
  • InSilico Medicine
  • Nvidia
  • twoXAR
  • Verge Genomics
  • Zebra Medical Vision

 

The opinions and insights discussed in this report were influenced by valuable inputs from senior stakeholders in the industry. The report features detailed transcripts of interviews held with Mausumi Acharya (CEO, Advenio Technosys), Carla Leibowitz (Head of Strategy and Marketing, Arterys) and Deekshith Marla (CTO, Arya.ai). 

For additional details, please visit 

https://www.rootsanalysis.com/reports/view_document/deep-learning-in-drug-discovery-and-diagnostics-2017-2035/156.html

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Contact:

Gaurav Chaudhary

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