Deep Learning in Drug Discovery and Diagnostics Market - Global Industry Insights, Trends, Outlook, and Opportunity Analysis, 2018-2026

  • To Be Published : Oct 2019 |
  • Format : CMI Insight PPT Format CMI Insight PDF Format

Deep learning is machine learning that analyzes large volumes of labeled and unlabeled data along with multi-dimensional and complex data with non-trivial patterns. It is touted to be a replacement for manual feature engineering with unsupervised feature learning. Massive influx of multimodality data in recent times further necessitates use of artificial intelligence for data analytics in health information systems. This in turn has impelled rise in deployment of analytical data-driven models generation, which are based on machine learning in health informatics. This is expected to be one of the vital factors supporting growth of deep learning in drug discovery and diagnostics market in the near future. Deep learning in drug discovery and diagnostics market is an upcoming technique deeply rooted in artificial neural networks and is expected to gain traction in the near future. It is expected to evolve as an important tool deep learning about the healthcare information system and would be utilized to restructure the future of healthcare sector and artificial intelligence. Rapid developments in computer-based operations and efficient and quick data storage are also contributing to fast uptake of the technology. The technique automatically generates optimum high level features with semantic effective input data interpretation, which is expected to support growth of deep learning in drug discovery and diagnostics market over the forecast period (2016–2024).

Deep Learning in Drug Discovery and Diagnostics Market Taxonomy:

By Application

  • Drug Discovery
  • Diagnostics
  • Forensic Interventions
  • Others

By End-use Industry

  • Pharmaceutical Companies
  • Biotechnology Companies
  • Contract Research Organizations
  • Healthcare IT

The provision of reducing time interval in drug discovery is expected to underpin the growth of deep learning in drug discovery and diagnostics market:

Conventionally, drug discovery and drug development was considered to be a complex and time consuming process. Various analytical approaches are being used to further usher in developments. Latest methods such as data mining, homology modeling, conventional machine learning and its biologically inspired branch technique, deep learning are the sources for next-generation drug discovery methods. The abovementioned reason is projected to fuel growth rate of deep learning in drug discovery and diagnostics market. Furthermore, healthcare and life sciences organizations are leveraging artificial intelligence and deep learning approach to enhance their product portfolio.

Pharmaceutical companies and other drug manufacturers are focusing on integrating in deep learning in drug discovery and diagnostics to introduce novel treatments to effectively address the increasing burden of diseases. This would help ensure that prospective drugs would attack the source of any ailment along with satisfaction of restrictive metabolic and toxic constraints. As mentioned earlier, drug discovery involves significant investment of time and resources, and the outcome is rather uncertain. Deep learning in drug discovery and diagnostics plays a pivotal role in increasing the probability of getting a successful outcome. This is expected to be a crucial driver for the deep learning in drug discovery and diagnostics market over the forecast period.

Rise in number of application is projected to favor growth of the deep learning in drug discovery and diagnostics market:

The global deep learning in drug discovery and diagnostics market is consolidated, with major players holding up the maximum share due to their extensive expertise in the subject of artificial intelligence, attained through several score years of intensive studies. Players in the market are developing novel techniques to understand the nature of the diagnostic biomarkers and drug discovery through major spending on R&D. For instance, Google Inc. is making significant inroads in better understanding of daily health and wellbeing habits to reach out to the global healthcare concerns in the best possible way.

Key players operating in the deep learning in drug discovery and diagnostics market include Google Inc., IBM Corp., Microsoft Corporation, Qualcomm Technologies, Inc., General Vision Inc., Insilico Medicine, Inc., NVIDIA Corporation, Zebra Medical Vision, Inc., Enlitic, Ginger.io, MedAware and Lumiata.

Key Developments

Research and development activities related to deep learning in drug discovery and diagnostics is expected to boost the market growth. For instance, on September 2, 2019, Insilico Medicine Hong Kong Ltd. reported development of a deep generative model, generative tensorial reinforcement learning (GENTRL), for de novo small-molecule design. GENTRL was used to discover potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis and other diseases, in 21 days.

Key players in the market are focused on adopting collaboration and partnership strategies to enter in the emerging market. For instance, in February 2019, Juvenescence AI, Ltd., a drug development company focused on combating ageing and age-related diseases, collaborated with NetraMark Corp., a company that uses machine learning algorithms to redesign failing drugs, to form a joint venture, NetraPharma.

Major market players are also focused on raising funding to support their product development. For instance, in August 2019, Verisim Life, Inc., a U.S.-based biotechnology startup that uses AI-powered biosimulations to replace animal drug testing, announced it has raised $5.2 million in a round of funding led by Serra Ventures and OCA Ventures.

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