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AI in Drug Discovery Market 2022 | Industry Demand, Top Key Players Data, Future Innovation Sales, Consumption Status

Some of the vital players within the space of AI in Drug Discovery are as follows;, IBM Corporation, Microsoft Corporation, Google Inc., NVIDIA Corporation, Pfizer, Merck, GSK, Novartis, AstraZeneca, Abbvie, Elli Lilly, Atomwise, Inc, Deep Genomics, Cloud Pharmaceuticals, Exscientia, Cyclica, Numerate, Envisagenics, OWKIN, Inc., Verge Genomics

The market value of AI in Drug Discovery recorded in 2020 is $343.78 million and predictions are made on its future growth at a CAGR of 43.24%

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Market Overview

AI in Drug Discovery Market By Component (Software, Service), Technology (ML, DL), By Application (Immuno-oncology, Neurodegenerative Diseases, Cardiovascular Diseases, Metabolic Diseases, and Other Applications), By End User (Pharmaceutical & Biotechnology, CRO, and Research Centers and Academic & Government Institutes), By Geography (North America, Europe, APAC, and RoW) Global Opportunity Analysis and Industry Forecast up to 2026

Artificial intelligence is the simulation of human intelligence in machines that are designed to think like humans and copy their actions. This gives potential to transform the pharmaceutical industry as well. Many pharma competitors are now investing in this technology at some level.

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Research Methodology:
The AI in drug delivery market has been analyzed by utilizing the optimum combination of secondary sources and in-house methodology, along with an irreplaceable blend of primary insights. The real-time assessment of the market is an integral part of our market sizing and forecasting methodology. Our industry experts and panel of primary participants have helped in compiling relevant aspects with realistic parametric estimations for a comprehensive study. The participation share of different categories of primary participants is given below:

There is a significant increase in the number of applications that focuses on the target and drug discovery, preclinical and clinical development, and post approval activities. AI can help in improving the drug approval rates, reduce development costs, swift availability of medications and help patients comply with their treatments.

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The global AI in the drug discovery market has a high demand due to its increasing need to shorten the drug discovery process in order to get drugs faster for treating various incurable and viral diseases. Moreover, AI plays an important role to understand the mechanism of disease by determining biomarkers and creating data or models for the drug discovery process which will ultimately drive the market over the next few years.

To use the opportunity of diversifying the drug pipelines, AI-enabled prediction tools can improve the speed, accuracy, and preclinical testing and also it can open new research leads by availing more competitive R&D strategies. Failure to demonstrate value compared to available therapies is a key factor undermining clinical trial progression. Finding new niches of competitive advantage could reduce withdrawals and improve asset sales.

AI in drug discovery research report is segregated into following segments:
By Component
By Technology
Machine Learning
Deep Learning
By Application
Neurodegenerative Diseases
Cardiovascular Diseases
Metabolic Diseases
Other Applications
By End User
Pharmaceutical & Biotechnology Companies
Contract Research Organizations
Research Centers and Academic & Government Institutes

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The market value of AI in Drug Discovery recorded in 2020 is $343.78 million and predictions are made on its future growth at a CAGR of 43.24%. There are four major factors which are the result of its increasing growth:

Requisite control on Cost and Time of Drug Discovery & Development
Demanding requirement of precision medicine
Expanding Cross-Industry Collaborations and Partnerships
Growing number of troublemakers of AI drug discovery

AI in drug discovery has enlightened the productivity of medical facilities, and on the same page has helped in boosting care facilities. Extensive medical facilities always try to opt for better services and latest technologies with minimum space for mistakes. This technology has fastened the drug construction procedures for cases which need specialized medicinal attention, which subsequently decreases failure risks and also the cost of research & development procedures.

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Another important factor of reducing the drug price has pressured the drug manufacturers to boost the production of AI for the drug discovery market. Efficient working of AI reduced the failure rate of clinical trials and also eradicated the cost of length research and development in drug discovery. Another factor which can bolster the anticipated growth of the Global AI for the drug discovery market is the lack of skilled health care professionals.

Currently, only one out of ten drugs are approved after clinical trials. Mostly because of lack of effectiveness and safety issues. Considering the growing cost of bringing a drug into the market, a minimum of ten percent improvement in the prediction of accuracy can save billions of dollars invested on drug development for AI in Drug Discovery vendors which compensate for both the residential as well as commercial sectors. So, improving the accuracy of predictions on the efficacy and safety of drugs is highly required. With a predicted CAGR of 43.24%, we have a lot of opportunities at our disposal and this report will help in understanding the current market dynamics, changing needs, and innovations that might be needed to make the user experience enriching.

This report would be the foundation for any research on the AI in Drug Discovery, vendor capabilities, SWOT of the vendors and framework for data analysis for further advanced innovation

An insight to the major competitors in the market, their journey and the competitive edge which one should have to beat other players is given
The report contains an understanding of technological innovations and advanced solutions for the AI in Drug Discovery.

Table of Content:

  • Market Definition and Overview
  • Research Method and Logic
  • Market Competition Analysis
  • Product and Service Analysis
  • Strategies for Company to Deal with the Impact of COVID-19
  • Market Segment by Type, Historical Data and Market Forecasts
  • Market Segment by Application, Historical Data and Market Forecasts
  • Market by by Region, Historical Data and Market Forecasts
  • Market Dynamic Analysis and Development Suggestions

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