Portland, OR , April 20, 2023 (GLOBE NEWSWIRE) -- According to the report published by Allied Market Research, the global machine learning in pharmaceutical industry market garnered $1.2 billion in 2021, and is estimated to generate $26.2 billion by 2031, manifesting a CAGR of 37.9% from 2022 to 2031. The report provides an extensive analysis of changing market dynamics, major segments, value chain, competitive scenario, and regional landscape. This research offers a valuable guidance to leading players, investors, shareholders, and startups in devising strategies for the sustainable growth and gaining competitive edge in the market.
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Report coverage & details
| Report coverage | Details |
| Forecast Period | 2022–2031 |
| Base Year | 2021 |
| Market Size in 2021 | $1.2 billion |
| Market Size in 2031 | $26.2 billion |
| CAGR | 37.9% |
| No. of Pages in Report | 280 |
| Segments covered | Component, Enterprise size, Deployment, and Region |
| Drivers | The diverse applications of machine learning to predict the efficacy and safety of potential new drugs |
| Reduction in time and cost involved in the drug discovery and development process by machine learning | |
| Opportunities | ML is particularly useful for identifying drug candidates that have a high likelihood of success, potentially reducing the risk of failure during clinical trials |
| Restraints | High cost & complexity of data collection |
Covid-19 Scenario:
- The COVID-19 pandemic had a positive impact on the growth of the global machine learning in pharmaceutical industry market, owing to the role of machine learning in drug discovery and development. Machine learning algorithms have been increasingly used in drug discovery and development for several years, and the pandemic accelerated this trend.
- The urgency of finding treatments and vaccines for COVID-19 led to a rise in investment in machine learning and artificial intelligence for drug development. Machine learning was used to rapidly analyze large amounts of data related to the coronavirus and potential treatments.
- With many people unable or unwilling to participate in traditional clinical trials due to COVID-19 concerns, virtual trials became more common.
The research provides detailed segmentation of the global machine learning in pharmaceutical industry market based on component, enterprise size, deployment, and region. The report discusses segments and their sub-segments in detail with the help of tables and figures. Market players and investors can strategize according to the highest revenue-generating and fastest-growing segments mentioned in the report.
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Based on component, the solution segment held the highest share in 2021, accounting for more than two-thirds of the global machine learning in pharmaceutical industry market and is expected to continue its leadership status during the forecast period. However, the services segment is expected to register the highest CAGR of 39.5% from 2022 to 2031.
On the basis of enterprise size, the large enterprises segment accounted for the highest share in 2021, contributing to around three-fourths of the global machine learning in pharmaceutical industry market, and is expected to maintain its lead in terms of revenue during the forecast period. Moreover, the SMEs segment is expected to manifest the highest CAGR of 40.1% from 2022 to 2031.
Based on deployment, the cloud segment accounted for the highest share in 2021, holding more than two-thirds of the global machine learning in pharmaceutical industry market, and is expected to continue its leadership status during the forecast period. This segment is estimated to grow at the highest CAGR of 40.0% during the forecast period. The report also discusses on-premise segment.
Based on region, North America held the largest share in 2021, contributing to nearly half of the global machine learning in pharmaceutical industry market share, and is projected to maintain its dominant share in terms of revenue in 2031. In addition, the Asia-Pacific region is expected to manifest the fastest CAGR of 42.4% during the forecast period. The report also analyzes the markets in Europe and LAMEA regions.
Leading market players of the global machine learning in pharmaceutical industry market analyzed in the research include BioSymetrics Inc., Deep Genomics, Atomwise Inc., NVIDIA Corporation, International Business Machines Corporation, Microsoft Corporation, IBM, cyclica inc., Cloud Pharmaceuticals, Inc., and Alphabet Inc.
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The report provides a detailed analysis of these key players of the global machine learning in pharmaceutical industry market. These players have adopted different strategies such as new product launches, collaborations, expansion, joint ventures, agreements, and others to increase their market share and maintain dominant shares in different regions. The report is valuable in highlighting business performance, operating segments, product portfolio, and strategic moves of market players to showcase the competitive scenario.
About Us
Allied Market Research (AMR) is a full-service market research and business-consulting wing of Allied Analytics LLP based in Portland, Oregon. Allied Market Research provides global enterprises as well as medium and small businesses with unmatched quality of "Market Research Reports" and "Business Intelligence Solutions." AMR has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions and achieve sustainable growth in their respective market domain.
We are in professional corporate relations with various companies and this helps us in digging out market data that helps us generate accurate research data tables and confirms utmost accuracy in our market forecasting. Allied Market Research CEO Pawan Kumar is instrumental in inspiring and encouraging everyone associated with the company to maintain high quality of data and help clients in every way possible to achieve success. Each and every data presented in the reports published by us is extracted through primary interviews with top officials from leading companies of domain concerned. Our secondary data procurement methodology includes deep online and offline research and discussion with knowledgeable professionals and analysts in the industry.
