The machine learning in the pharmaceutical industry market size has grown exponentially in recent years. It will grow from $2.22 billion in 2023 to $3.02 billion in 2024 at a compound annual growth rate (CAGR) of 36.1%. The growth in the historic period can be attributed to the increased adoption of federated learning, accelerated drug discovery timelines, enhanced drug safety and pharmacovigilance, and the expansion of precision medicine applications.
The machine learning in the pharmaceutical industry market size is expected to see exponential growth in the next few years. It will grow to $10.2 billion in 2028 at a compound annual growth rate (CAGR) of 35.7%. The forecasted growth in the upcoming period can be attributed to several factors, including the increasing complexity of biological data, the growing computational power available, a rise in industry awareness and education, and the shift towards patient-centric healthcare solutions. Major trends expected during this period include partnerships and collaborations between AI and pharmaceutical companies, the development of drug-agnostic therapies, efforts to improve the interoperability of machine learning systems, the implementation of decentralized clinical trials, and the use of AI for biomarker discovery.
The growing use of artificial intelligence (AI) is fueling the expansion of machine learning (ML) in the pharmaceutical sector. AI, which mimics human cognition to execute intricate tasks such as analysis, reasoning, and learning, is a key driver behind ML, a subset of AI that employs algorithms trained on data to generate models capable of performing complex tasks. AI and ML have been harnessed in pharmaceutical technology and drug delivery design, providing accelerated solutions to intricate challenges. They hold the potential to revolutionize the drug delivery process, improve decision-making tools, and manage vast amounts of data for more effective decision-making. For example, as reported by Forbes in June 2023, around 432,000 UK organizations, or one in six, have adopted at least one AI technology. Furthermore, 68% of large businesses, 33% of medium-sized businesses, and 15% of small businesses have adopted at least one AI technology. Consequently, the increasing adoption of AI is poised to propel ML in the pharmaceutical industry.
Key players in the machine learning (ML) sector of the pharmaceutical industry are concentrating on creating user-friendly software platforms, including drug discovery software, to enhance their drug discovery capabilities. Drug discovery software encompasses various specialized tools and platforms utilized in identifying and developing new pharmaceutical drugs. For instance, in December 2023, Merck & Co. Inc., a US-based pharmaceutical company, introduced the AIDDISON drug discovery software. This software-as-a-service platform is the first of its kind, integrating drug discovery and synthesis through generative AI, ML, and computer-aided drug design. AIDDISON allows laboratories to pinpoint suitable drug candidates within a vast chemical space, virtually screen compounds from over 60 billion chemical targets, and assess synthesis routes for safer, more cost-effective, and higher-yield drug production.
In July 2023, BioNTech SE, a Germany-based biopharmaceutical company, acquired InstaDeep for $541 million. This acquisition is anticipated to enhance BioNTech's leading position in AI-powered drug discovery, design, and development. InstaDeep, a UK-based technology company, provides a variety of AI solutions and machine learning services tailored for the pharmaceutical sector.
Major companies operating in the machine learning (ml) in the pharmaceutical industry market report are Amazon.com Inc., Alphabet Inc., Microsoft Corporation, Dell Technologies Inc., Hitachi Ltd., International Business Machines Corporation, Cisco Systems Inc., Oracle Corporation, Honeywell International Inc., Hewlett Packard Enterprise, NVIDIA Corporation, Thales SA, Atos SE, Hexagon AB, Palantir Technologies Inc., Verient Systems Inc., Alteryx Inc., Comet ML Inc., GAVS Technologies, NEC Corporation, Veritone Inc., H2O.ai Inc., Sparkcognition Inc., Akira AI, Deep Genomics Inc., Cloud Pharmaceuticals Inc., Atomwise Inc., Cyclica Inc., BioSymetrics Inc., Neptune Labs.
North America was the largest region in the machine learning in the pharmaceutical industry market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning (ml) in the pharmaceutical industry market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning (ml) in the pharmaceutical industry market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Machine learning (ML) in the pharmaceutical industry market involves the application of artificial intelligence (AI) algorithms and techniques to enhance drug discovery, development, and manufacturing processes. Regulatory compliance and overcoming challenges related to data quality, research gaps, and regulatory uncertainty are essential for implementing ML solutions in this industry.
The primary components of machine learning in the pharmaceutical industry are solutions and services. Solutions are applications or systems that use ML algorithms to tackle specific challenges or tasks in the pharmaceutical sector. These solutions can be deployed on the cloud or on-premise and cater to enterprises of various sizes, including small and medium enterprises (SMEs) and large enterprises.
The machine learning (ML) in the pharmaceutical industry market research report is one of a series of new reports that provides machine learning (ML) in the pharmaceutical industry market statistics, including machine learning (ML) in the pharmaceutical industry global market size, regional shares, competitors with machine learning (ML) in the pharmaceutical industry market share, detailed machine learning (ML) in the pharmaceutical industry market segments, market trends, and opportunities, and any further data you may need to thrive in the machine learning (ML) in the pharmaceutical industry. This machine learning (ML) in the pharmaceutical industry market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.
The machine learning (ML) in the pharmaceutical industry market consists of revenues earned by entities by providing components of machine learning (ML) in the pharmaceutical industry market such as solutions and services. The market value includes the value of related goods sold by the service provider or included within the service offering. The machine learning (ML) in the pharmaceutical industry market also includes sales of central processing units (CPUs), random access memory, storage systems, and graphics processing units (GPUs). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 3-5 business days.
The machine learning in the pharmaceutical industry market size is expected to see exponential growth in the next few years. It will grow to $10.2 billion in 2028 at a compound annual growth rate (CAGR) of 35.7%. The forecasted growth in the upcoming period can be attributed to several factors, including the increasing complexity of biological data, the growing computational power available, a rise in industry awareness and education, and the shift towards patient-centric healthcare solutions. Major trends expected during this period include partnerships and collaborations between AI and pharmaceutical companies, the development of drug-agnostic therapies, efforts to improve the interoperability of machine learning systems, the implementation of decentralized clinical trials, and the use of AI for biomarker discovery.
The growing use of artificial intelligence (AI) is fueling the expansion of machine learning (ML) in the pharmaceutical sector. AI, which mimics human cognition to execute intricate tasks such as analysis, reasoning, and learning, is a key driver behind ML, a subset of AI that employs algorithms trained on data to generate models capable of performing complex tasks. AI and ML have been harnessed in pharmaceutical technology and drug delivery design, providing accelerated solutions to intricate challenges. They hold the potential to revolutionize the drug delivery process, improve decision-making tools, and manage vast amounts of data for more effective decision-making. For example, as reported by Forbes in June 2023, around 432,000 UK organizations, or one in six, have adopted at least one AI technology. Furthermore, 68% of large businesses, 33% of medium-sized businesses, and 15% of small businesses have adopted at least one AI technology. Consequently, the increasing adoption of AI is poised to propel ML in the pharmaceutical industry.
Key players in the machine learning (ML) sector of the pharmaceutical industry are concentrating on creating user-friendly software platforms, including drug discovery software, to enhance their drug discovery capabilities. Drug discovery software encompasses various specialized tools and platforms utilized in identifying and developing new pharmaceutical drugs. For instance, in December 2023, Merck & Co. Inc., a US-based pharmaceutical company, introduced the AIDDISON drug discovery software. This software-as-a-service platform is the first of its kind, integrating drug discovery and synthesis through generative AI, ML, and computer-aided drug design. AIDDISON allows laboratories to pinpoint suitable drug candidates within a vast chemical space, virtually screen compounds from over 60 billion chemical targets, and assess synthesis routes for safer, more cost-effective, and higher-yield drug production.
In July 2023, BioNTech SE, a Germany-based biopharmaceutical company, acquired InstaDeep for $541 million. This acquisition is anticipated to enhance BioNTech's leading position in AI-powered drug discovery, design, and development. InstaDeep, a UK-based technology company, provides a variety of AI solutions and machine learning services tailored for the pharmaceutical sector.
Major companies operating in the machine learning (ml) in the pharmaceutical industry market report are Amazon.com Inc., Alphabet Inc., Microsoft Corporation, Dell Technologies Inc., Hitachi Ltd., International Business Machines Corporation, Cisco Systems Inc., Oracle Corporation, Honeywell International Inc., Hewlett Packard Enterprise, NVIDIA Corporation, Thales SA, Atos SE, Hexagon AB, Palantir Technologies Inc., Verient Systems Inc., Alteryx Inc., Comet ML Inc., GAVS Technologies, NEC Corporation, Veritone Inc., H2O.ai Inc., Sparkcognition Inc., Akira AI, Deep Genomics Inc., Cloud Pharmaceuticals Inc., Atomwise Inc., Cyclica Inc., BioSymetrics Inc., Neptune Labs.
North America was the largest region in the machine learning in the pharmaceutical industry market in 2023. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in the machine learning (ml) in the pharmaceutical industry market report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East, Africa. The countries covered in the machine learning (ml) in the pharmaceutical industry market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.
Machine learning (ML) in the pharmaceutical industry market involves the application of artificial intelligence (AI) algorithms and techniques to enhance drug discovery, development, and manufacturing processes. Regulatory compliance and overcoming challenges related to data quality, research gaps, and regulatory uncertainty are essential for implementing ML solutions in this industry.
The primary components of machine learning in the pharmaceutical industry are solutions and services. Solutions are applications or systems that use ML algorithms to tackle specific challenges or tasks in the pharmaceutical sector. These solutions can be deployed on the cloud or on-premise and cater to enterprises of various sizes, including small and medium enterprises (SMEs) and large enterprises.
The machine learning (ML) in the pharmaceutical industry market research report is one of a series of new reports that provides machine learning (ML) in the pharmaceutical industry market statistics, including machine learning (ML) in the pharmaceutical industry global market size, regional shares, competitors with machine learning (ML) in the pharmaceutical industry market share, detailed machine learning (ML) in the pharmaceutical industry market segments, market trends, and opportunities, and any further data you may need to thrive in the machine learning (ML) in the pharmaceutical industry. This machine learning (ML) in the pharmaceutical industry market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenarios of the industry.
The machine learning (ML) in the pharmaceutical industry market consists of revenues earned by entities by providing components of machine learning (ML) in the pharmaceutical industry market such as solutions and services. The market value includes the value of related goods sold by the service provider or included within the service offering. The machine learning (ML) in the pharmaceutical industry market also includes sales of central processing units (CPUs), random access memory, storage systems, and graphics processing units (GPUs). Values in this market are ‘factory gate’ values, that is the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.
The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD, unless otherwise specified).
The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.
This product will be delivered within 3-5 business days.
Table of Contents
1. Executive Summary2. Machine Learning (ML) in The Pharmaceutical Industry Market Characteristics3. Machine Learning (ML) in The Pharmaceutical Industry Market Trends And Strategies32. Global Machine Learning (ML) in The Pharmaceutical Industry Market Competitive Benchmarking33. Global Machine Learning (ML) in The Pharmaceutical Industry Market Competitive Dashboard34. Key Mergers And Acquisitions In The Machine Learning (ML) in The Pharmaceutical Industry Market
4. Machine Learning (ML) in The Pharmaceutical Industry Market - Macro Economic Scenario
5. Global Machine Learning (ML) in The Pharmaceutical Industry Market Size and Growth
6. Machine Learning (ML) in The Pharmaceutical Industry Market Segmentation
7. Machine Learning (ML) in The Pharmaceutical Industry Market Regional And Country Analysis
8. Asia-Pacific Machine Learning (ML) in The Pharmaceutical Industry Market
9. China Machine Learning (ML) in The Pharmaceutical Industry Market
10. India Machine Learning (ML) in The Pharmaceutical Industry Market
11. Japan Machine Learning (ML) in The Pharmaceutical Industry Market
12. Australia Machine Learning (ML) in The Pharmaceutical Industry Market
13. Indonesia Machine Learning (ML) in The Pharmaceutical Industry Market
14. South Korea Machine Learning (ML) in The Pharmaceutical Industry Market
15. Western Europe Machine Learning (ML) in The Pharmaceutical Industry Market
16. UK Machine Learning (ML) in The Pharmaceutical Industry Market
17. Germany Machine Learning (ML) in The Pharmaceutical Industry Market
18. France Machine Learning (ML) in The Pharmaceutical Industry Market
19. Italy Machine Learning (ML) in The Pharmaceutical Industry Market
20. Spain Machine Learning (ML) in The Pharmaceutical Industry Market
21. Eastern Europe Machine Learning (ML) in The Pharmaceutical Industry Market
22. Russia Machine Learning (ML) in The Pharmaceutical Industry Market
23. North America Machine Learning (ML) in The Pharmaceutical Industry Market
24. USA Machine Learning (ML) in The Pharmaceutical Industry Market
25. Canada Machine Learning (ML) in The Pharmaceutical Industry Market
26. South America Machine Learning (ML) in The Pharmaceutical Industry Market
27. Brazil Machine Learning (ML) in The Pharmaceutical Industry Market
28. Middle East Machine Learning (ML) in The Pharmaceutical Industry Market
29. Africa Machine Learning (ML) in The Pharmaceutical Industry Market
30. Machine Learning (ML) in The Pharmaceutical Industry Market Competitive Landscape And Company Profiles
31. Machine Learning (ML) in The Pharmaceutical Industry Market Other Major And Innovative Companies
35. Machine Learning (ML) in The Pharmaceutical Industry Market Future Outlook and Potential Analysis
36. Appendix
Executive Summary
Machine Learning (ML) in The Pharmaceutical Industry Global Market Report 2024 provides strategists, marketers and senior management with the critical information they need to assess the market.This report focuses on machine learning (ML) in the pharmaceutical industry market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.
Reasons to Purchase:
- Gain a truly global perspective with the most comprehensive report available on this market covering 50+ geographies.
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- Assess the Russia - Ukraine war’s impact on agriculture, energy and mineral commodity supply and its direct and indirect impact on the market.
- Measure the impact of high global inflation on market growth.
- Create regional and country strategies on the basis of local data and analysis.
- Identify growth segments for investment.
- Outperform competitors using forecast data and the drivers and trends shaping the market.
- Understand customers based on the latest market shares.
- Benchmark performance against key competitors.
- Suitable for supporting your internal and external presentations with reliable high quality data and analysis
- Report will be updated with the latest data and delivered to you along with an Excel data sheet for easy data extraction and analysis.
- All data from the report will also be delivered in an excel dashboard format.
Description
Where is the largest and fastest growing market for machine learning (ML) in the pharmaceutical industry? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward? The machine learning (ML) in the pharmaceutical industry market global report answers all these questions and many more.The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market’s historic and forecast market growth by geography.
- The market characteristics section of the report defines and explains the market.
- The market size section gives the market size ($b) covering both the historic growth of the market, and forecasting its development.
- The forecasts are made after considering the major factors currently impacting the market. These include:
- The impact of sanctions, supply chain disruptions, and altered demand for goods and services due to the Russian Ukraine war, impacting various macro-economic factors and parameters in the Eastern European region and its subsequent effect on global markets.
- The impact of higher inflation in many countries and the resulting spike in interest rates.
- The continued but declining impact of COVID-19 on supply chains and consumption patterns.
- Market segmentations break down the market into sub markets.
- The regional and country breakdowns section gives an analysis of the market in each geography and the size of the market by geography and compares their historic and forecast growth. It covers the growth trajectory of COVID-19 for all regions, key developed countries and major emerging markets.
- The competitive landscape chapter gives a description of the competitive nature of the market, market shares, and a description of the leading companies. Key financial deals which have shaped the market in recent years are identified.
- The trends and strategies section analyses the shape of the market as it emerges from the crisis and suggests how companies can grow as the market recovers.
Scope
Markets Covered:
1) By Component: Solution; Services2) By Component: Cloud; On-premise
3) By Enterprise Size: Small and Medium Enterprises (SMEs); Large Enterprises
Key Companies Mentioned: Amazon.com Inc.; Alphabet Inc.; Microsoft Corporation; Dell Technologies Inc.; Hitachi Ltd.
Countries: Australia; Brazil; China; France; Germany; India; Indonesia; Japan; Russia; South Korea; UK; USA; Canada; Italy; Spain
Regions: Asia-Pacific; Western Europe; Eastern Europe; North America; South America; Middle East; Africa
Time Series: Five years historic and ten years forecast.
Data: Ratios of market size and growth to related markets, GDP proportions, expenditure per capita.
Data Segmentation: Country and regional historic and forecast data, market share of competitors, market segments.
Sourcing and Referencing: Data and analysis throughout the report is sourced using end notes.
Delivery Format: PDF, Word and Excel Data Dashboard.
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Amazon.com Inc.
- Alphabet Inc.
- Microsoft Corporation
- Dell Technologies Inc.
- Hitachi Ltd.
- International Business Machines Corporation
- Cisco Systems Inc.
- Oracle Corporation
- Honeywell International Inc.
- Hewlett Packard Enterprise
- NVIDIA Corporation
- Thales SA
- Atos SE
- Hexagon AB
- Palantir Technologies Inc.
- Verient Systems Inc.
- Alteryx Inc.
- Comet ML Inc.
- GAVS Technologies
- NEC Corporation
- Veritone Inc.
- H2O.ai Inc.
- Sparkcognition Inc.
- Akira AI
- Deep Genomics Inc.
- Cloud Pharmaceuticals Inc.
- Atomwise Inc.
- Cyclica Inc.
- BioSymetrics Inc.
- Neptune Labs
Methodology
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Table Information
| Report Attribute | Details |
|---|---|
| No. of Pages | 175 |
| Published | April 2024 |
| Forecast Period | 2024 - 2028 |
| Estimated Market Value ( USD | $ 3.02 Billion |
| Forecasted Market Value ( USD | $ 10.2 Billion |
| Compound Annual Growth Rate | 35.7% |
| Regions Covered | Global |
