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Deep Learning in Drug Discovery and Diagnostics Market
Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.
SEATTLE, WASHINGTON, UNITED STATES, December 15, 2021 /EINPresswire.com / -- Another factual information on the 'Deep Learning in Drug Discovery and Diagnostics Market ' has as of late added by CMI to its storehouse. This research report offers an inside and out examination of various perspectives, for example, market patterns, pieces of the overall industry, drivers, restrictions, opportunities. It gives an investigative view to settle on further choices in organizations. It covers comprehensive instructive information of various key players working over the globe.
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Overview:
Deep learning is a type of machine learning (ML) and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge. It is known as the replacement of manual feature engineering with unsupervised feature learning. In the recent past, the large-scale influx of multimodal data has increased the need for the use AI for data analysis in health information systems. This in turn has increased the deployment of analytical data-driven model generation, based on ML, in health informatics. This in turn is expected to boost the growth of the global deep learning in drug discovery and diagnostics market during the forecast period.
Deep learning can be incorporated in all steps of the process of drug discovery. For example, machine learning algorithms have been used to find a new use of drugs, discover drug efficacy, optimize the bioactivity of molecules, and predict drug-protein interactions, among others. Deep learning in drug discovery and diagnostics has emerged as a novel technique in the recent times and is expected to gain traction worldwide. It can be used to restructure the future of the healthcare industry. Developments in computer-based operations and quick and efficient data storage are also contributing to the rapid advance of technology. The technology automatically generates optimal high level features with semantic effective input data interpretation, which in turn is expected to augment the growth of the market.
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Drivers:
Need for quick and efficient drug discovery is expected to augment growth of the global deep learning in drug discovery and diagnostics market over the forecast period. Drug discovery is a complex and time-consuming process. Methods such as deep learning, conventional machine learning, homology modeling, and data mining are the sources for next-gen drug discovery. Deep learning helps reduce drug discovery time. This is expected to drive the market growth.
Furthermore, healthcare and life sciences organizations are leveraging deep learning and AI to strengthen their product portfolio. Drug makers or pharma companies are taking advantage of deep learning to launch new treatments to effectively address the growing burden of diseases. This in turn is expected to help ensure that potential drugs will attack the source of any disease, along with the satisfaction of restrictive toxic and metabolic barriers. Deep learning plays an important role in increasing the chances of getting a successful result. This in turn is expected to aid in the growth of the market during the forecast period.
Moreover, increasing number of application is expected to propel growth of the global deep learning in drug discovery and diagnostics market. The market is consolidated, with the market players holding up the maximum share due to their wide expertise in the subject of artificial intelligence gained through years of in-depth study. For instance, market players are focusing on developing new technologies to understand the nature of drug discovery through extensive research and development.
Market Taxonomy:
By Application:
Drug discovery
Diagnostics
Forensic interventions and Others
By End-use Industry:
Pharmaceutical Companies
Biotechnology Companies
Contract Research Organizations
Healthcare IT
Competitive Landscape:
Key players active in the global deep learning in drug discovery and diagnostics market are Lumiata, MedAware, Ginger.io, Enlitic, Zebra Medical Vision, Inc., NVIDIA Corporation, Insilico Medicine, Inc., General Vision Inc., IBM Corp., Microsoft Corporation, Google Inc., and Qualcomm Technologies, Inc., among others.
Key Developments:
Market player are adopting various strategies, such as partnership and collaboration, and are engaged in research and development to launch new products and to gain competitive edge in the market. For instance, in September 2019, Insilico Medicine developed GENTRL, a new artificial intelligence system for drug discovery that dramatically accelerates the process from years to days. The company successfully tested the technology by creating a series of entirely new molecules capable of combating disorders like fibrosis.
In November 2018, NetraMark Corp. and Juvenescence Ltd. partnered to form NetraPharma, a joint venture that is using AI to develop therapies.
In August 2019, Verisim Life, Inc. raised US$ 5.2 million to build digital human and animal simulations to avoid unnecessary drug testing in animals.
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