Deep learning is a type of machine learning that can analyze large amounts of data, both labeled and unlabeled, that have complex and multi-dimensional patterns. It is often considered as a substitute for manual feature engineering, since it can learn unsupervised features. With the growing amount of data in various forms, such as audio, text, and images, there is an increasing need to utilize artificial intelligence for data analysis in health information systems.
At the same time, restraining factors that are expected to obstruct or hold the growth of the industry are also presented by our expert analysts in order to provide the key market players with a detailed scenario of future threats in advance. Furthermore, the report provides a quantitative and qualitative analysis of the market and outlines the pain point analysis, value chain analysis, and key regulations.
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The increasing demand for analytical data-driven models in health informatics has led to the adoption of machine learning in this field. This trend is expected to play a crucial role in the growth of the deep learning market for drug discovery and diagnostics. Deep learning is a novel technique that relies on artificial neural networks, and it is expected to gain more popularity in the future. This technique has the potential to revolutionize the healthcare industry by enabling a better understanding of healthcare information systems. Additionally, the rapid advancements in computer technology and efficient data storage have also contributed to the widespread adoption of deep learning in drug discovery and diagnostics.
Deep learning in drug discovery and diagnostics market is anticipated to increase as a result of the ability to reduce time interval in drug discovery:
In the past, drug discovery and development were known to be challenging and time-consuming processes. However, advancements in analytical approaches have facilitated the development of next-generation drug discovery methods. Techniques such as data mining, homology modeling, conventional machine learning, and its biologically-inspired branch, deep learning, are some of the latest methods being utilized. The increasing adoption of these techniques is expected to boost the growth of the deep learning market for drug discovery and diagnostics. Healthcare and life sciences organizations are also embracing artificial intelligence and deep learning approaches to improve their product portfolio.
Pharmaceutical companies and drug manufacturers are currently prioritizing the integration of deep learning in drug discovery and diagnostics to develop new and effective treatments that can address the growing burden of diseases. This approach can help ensure that prospective drugs target the root cause of the disease while also satisfying metabolic and toxic constraints. Since drug discovery is a time-consuming and resource-intensive process with uncertain outcomes, the application of deep learning in drug discovery and diagnostics can significantly increase the chances of success. This is expected to be a significant driver for the growth of the deep learning in drug discovery and diagnostics market in the forecast period.
Increase in applications is anticipated to support market growth for deep learning in drug discovery and diagnostics:
The global deep learning in drug discovery and diagnostics market is dominated by major players who have acquired extensive expertise in artificial intelligence through years of intensive research. These players are investing heavily in research and development to develop new techniques for understanding diagnostic biomarkers and drug discovery. One such example is Google Inc., which is making significant efforts to understand daily health and wellbeing habits to address global healthcare concerns more effectively. As a result, the market is highly consolidated, with major players holding the majority share.
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IBM Corp., Microsoft Corporation, Qualcomm Technologies, Inc., Google Inc., NVIDIA Corporation, General Vision Inc., Insilico Medicine, Inc., Zebra Medical Vision, Inc., Enlitic, Ginger.io, MedAware, and Lumiata are some of the key players in the deep learning in drug discovery and diagnostics market.
Key Developments
The growth of the deep learning in drug discovery and diagnostics market is expected to be driven by research and development activities. For example, on September 2, 2019, Insilico Medicine Hong Kong Ltd. announced the development of GENTRL, a deep generative model for de novo small-molecule design. GENTRL was successfully used to identify potent inhibitors of discoidin domain receptor 1 (DDR1), a kinase target linked to fibrosis and other diseases, within a span of just 21 days.
To penetrate the emerging market, major players in the industry are concentrating on implementing partnership and collaboration strategies. A case in point is the collaboration between Juvenescence AI, Ltd. and NetraMark Corp. in February 2019. Juvenescence AI, Ltd., which specializes in drug development to counter ageing and age-related illnesses, teamed up with NetraMark Corp., a company that utilizes machine learning algorithms to restructure ineffective drugs, to establish a joint venture named NetraPharma
Raising funds to support product development is also a key focus of major players in the market. For example, Verisim Life, Inc., a biotech startup based in the United States that uses AI-powered biosimulations to replace animal drug testing, raised $5.2 million in funding in August 2019. The funding round was led by Serra Ventures and OCA Ventures.
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Table of Content
Chapter 1 Industry Overview
1.1 Definition
1.2 Assumptions
1.3 Research Scope
1.4 Market Analysis by Regions
1.5 Deep Learning in Drug Discovery and Diagnostics Market Size Analysis from 2022 to 2028
11.6 COVID-19 Outbreak: Deep Learning in Drug Discovery and Diagnostics Industry Impact
Chapter 2 Global Deep Learning in Drug Discovery and Diagnostics Competition by Types, Applications, and Top Regions and Countries
2.1 Global Deep Learning in Drug Discovery and Diagnostics (Volume and Value) by Type
2.3 Global Deep Learning in Drug Discovery and Diagnostics (Volume and Value) by Regions
Chapter 3 Production Market Analysis
3.1 Global Production Market Analysis
3.2 Regional Production Market Analysis
Chapter 4 Global Deep Learning in Drug Discovery and Diagnostics Sales, Consumption, Export, Import by Regions (2017-2022)
Chapter 5 North America Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 6 East Asia Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 7 Europe Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 8 South Asia Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 9 Southeast Asia Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 10 Middle East Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 11 Africa Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 12 Oceania Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 13 South America Deep Learning in Drug Discovery and Diagnostics Market Analysis
Chapter 14 Company Profiles and Key Figures in Deep Learning in Drug Discovery and Diagnostics Business
Chapter 15 Global Deep Learning in Drug Discovery and Diagnostics Market Forecast (2022-2028)
Chapter 16 Conclusions
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