Rise in number of application isprojected to favor growth of the deep learning in drug discovery anddiagnostics market:
The global deep learning in DrugDiscovery and Diagnostics Market is consolidated, with major playersholding up the maximum share due to their extensive expertise in the subject ofartificial intelligence, attained through several score years of intensivestudies. Players in the market are developing novel techniques to understand thenature of the diagnostic biomarkers and drug discovery through major spendingon R&D. For instance, Google Inc. is making significant inroads in betterunderstanding of daily health and wellbeing habits to reach out to the globalhealthcare concerns in the best possible way.
Deep learning is machine learningthat analyzes large volumes of labeled and unlabeled data along withmulti-dimensional and complex data with non-trivial patterns. It is touted tobe a replacement for manual feature engineering with unsupervised featurelearning. Massive influx of multimodality data in recent times furthernecessitates use of artificial intelligence for data analytics in healthinformation systems. This in turn has impelled rise in deployment of analyticaldata-driven models generation, which are based on machine learning in healthinformatics. This is expected to be one of the vital factors supporting growthof deep learning in drug discovery and diagnostics market in the near future.Deep learning in drug discovery and diagnostics market is an upcoming techniquedeeply rooted in artificial neural networks and is expected to gain traction inthe near future. It is expected to evolve as an important tool deep learningabout the healthcare information system and would be utilized to restructurethe future of healthcare sector and artificial intelligence.
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Rapid developments incomputer-based operations and efficient and quick data storage are alsocontributing to fast uptake of the technology. The technique automaticallygenerates optimum high level features with semantic effective input datainterpretation, which is expected to support growth of deep learning in drugdiscovery and diagnostics market over the forecast period (2016–2024).
The provision of reducing timeinterval in drug discovery is expected to underpin the growth of deep learningin drug discovery and diagnostics market:
Conventionally, drug discoveryand 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 machinelearning and its biologically inspired branch technique, deep learning are the sourcesfor next-generation drug discovery methods. The abovementioned reason isprojected to fuel growth rate of deep learning in drug discovery anddiagnostics market. Furthermore, healthcare and life sciences organizations areleveraging artificial intelligence and deep learning approach to enhance theirproduct portfolio.
Pharmaceutical companies andother drug manufacturers are focusing on integrating in deep learning in drugdiscovery and diagnostics to introduce novel treatments to effectively addressthe increasing burden of diseases. This would help ensure that prospectivedrugs would attack the source of any ailment along with satisfaction ofrestrictive metabolic and toxic constraints. As mentioned earlier, drugdiscovery involves significant investment of time and resources, and theoutcome is rather uncertain. Deep learning in drug discovery and diagnosticsplays a pivotal role in increasing the probability of getting a successfuloutcome. This is expected to be a crucial driver for the deep learning in drugdiscovery and diagnostics market over the forecast period.
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Key players operating in the deeplearning in drug discovery and diagnostics market include Google Inc., IBMCorp., 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 developmentactivities related to deep learning in drug discovery and diagnostics isexpected to boost the market growth. For instance, on September 2, 2019,Insilico Medicine Hong Kong Ltd. reported development of a deep generativemodel, generative tensorial reinforcement learning (GENTRL), for de novosmall-molecule design. GENTRL was used to discover potent inhibitors ofdiscoidin domain receptor 1 (DDR1), a kinase target implicated in fibrosis andother diseases, in 21 days.
Key players in the market arefocused on adopting collaboration and partnership strategies to enter in theemerging market. For instance, in February 2019, Juvenescence AI, Ltd., a drugdevelopment company focused on combating ageing and age-related diseases,collaborated with NetraMark Corp., a company that uses machine learningalgorithms to redesign failing drugs, to form a joint venture, NetraPharma.
Major market players are alsofocused on raising funding to support their product development. For instance,in August 2019, Verisim Life, Inc., a U.S.-based biotechnology startup thatuses AI-powered biosimulations to replace animal drug testing, announced it hasraised $5.2 million in a round of funding led by Serra Ventures and OCAVentures.
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