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.
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.
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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).
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.
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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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