Leading pharmaceutical companies are actively involved in deploying AI for drug discovery. Taking AI in Pharmaceutical ecosystem into consideration, AI in drug discovery is one of the major applications. A lot of transformations occur across various applications such as in Diagnosis, Advanced medical device, Drug discovery, Hospital management etc. due to the technological advancement in the healthcare industry. —
In the year 2019, Eli Lilly has made an agreement to pay Atomwise up to US$ 1Mn per target discovery milestone. This partnership focusses on speeding up the drug development process that adds Atomwise’s advanced AI analytics over the Eli Lilly’s researched molecules. Atomwise Inc entered into evaluation agreement with Pfizer in 2018 to find identify drug candidates for target proteins. This partnership will help Pfizer to analyze the target proteins using AI technology. According to our estimates, the AI in Pharmaceutical Market is expected to witness a growth of approx. 23.1% CAGR from 2018 to 2023.
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Top Manufactures are included in AI in Pharmaceutical Market are Google LLC., Intel Corporation, Sanofi, Eli Lilly and Company, IBM Corporation, Atomwise
AI in Pharmaceutical Market: Segmentation
By Components: CPU, GPU, Microprocessor (including ASIC), FPGA, Memory, Modules, Storage.
By Technology: Machine Learning, NLP, Computer Vision, Others
By Devices: Smartphones and Tablets, Wearable, Workstation Systems, Medical Devices, Autonomous Robots, Imaging Systems, Others
By Deployment: Cloud, On-Premise
Exscientia, an UK based startup that offers drug discovery engine powered with AI technology. In 2017, Sanofi collaborate with Exscientia to discover small molecule and bispecific small-molecule candidates and focuses more on developing new drug combinations for diabetes and other metabolic diseases. In the year 2019, the company has collaborated with GSK specially to focus on therapeutic areas such as cardiovascular, oncology, metabolic diseases and other infectious diseases. GSK has agreed to pay US$ 43Mn to Excientia, if the 10 disease related target milestones are achieved. Hence the booming start-ups also contribute to the growth of AI in Pharmaceutical Market.
Apart from the drug discovery, AI can also be deployed on clinical trial processes. Technologies like NLP and Machine Learning are used to correlate diverse data from EHR, medical databases and trial database speed up the clinical trial. Finding the right patients for clinical trials is a huge and time-consuming process for the clinicians. IBM Watson uses Natural Language Processing (NLP) to match the patient criteria more accurately using the EHR data and quickly provide clinicians with ranked list of eligible patient information. Such initiatives will further strengthen the AI in Pharmaceutical market.
Drug adherence during the clinical trial causes unnecessary data mismatch and leading pharmaceutical companies are not able to prove the medicine’s efficacy to the real world. Hence it is important to monitor whether the patients are taking tablets as prescribed. AiCure, a New-York based company tracks the mobile phone of the patients and continuously records it. It uses facial recognition technology to identify the patient and monitor whether the patient has taken the tablet as prescribed. This help the pharmaceutical company to monitor patient’s adherence in a more effective manner. These are some of the benefits of adopting AI in Pharmaceutical Market. Currently the adoption of AI in Pharmaceutical is at an early stage and is expected to rise in future.
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