Machine Learning (ML) in The Pharmaceutical Industry Market Research Analysis 2024-2033 | Growth, Size, Forecast, Analysis, Insights

"The Business Research Company has recently revised its global market reports, now incorporating the most current data for 2024 along with projections extending up to 2033.

Machine Learning (ML) in The Pharmaceutical Industry Global Market Report 2024 by The Business Research Company offers comprehensive market insights, empowering businesses with a competitive edge. It includes detailed estimates for numerous segments and sub-segments, providing valuable strategic guidance.

The Market Size Is Expected To Reach $10.2 billion In 2028 At A CAGR Of 35.7% :

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 increased adoption of federated learning, accelerated drug discovery timelines, enhanced drug safety and pharmacovigilance, 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 growth in the forecast period can be attributed to increasing complexity of biological data, growing computational power, increasing industry awareness and education, patient-centric healthcare solutions. Major trends in the forecast period include AI-pharma partnerships and collaborations, drug-agnostic therapies, interoperability of ML systems, decentralized clinical trials, AI-driven biomarker discovery.

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Machine Learning (ML) in The Pharmaceutical Industry Market Major Segments
The machine learning (ml) in the pharmaceutical industry market covered in this report is segmented -

1) By Component: Solution, Services
2) By Component: Cloud, On-premise
3) By Enterprise Size : Small and Medium Enterprises (SMEs), Large Enterprises

Key Driver - Transformative Role Of Artificial Intelligence (AI) And Machine Learning (ML) In The Pharmaceutical Industry

The increasing adoption of artificial intelligence (AI) is driving machine learning (ML) in the pharmaceutical industry. AI is computer software that mimics human cognition to perform complex tasks, such as analyzing, reasoning, and learning, while ML is a subset of AI that uses algorithms trained on data to produce models that can perform complex tasks. AI and ML has been utilized in pharmaceutical technology and drug delivery design, offering expedited solutions to complex challenges. It has the potential to transform the drug delivery process, enhance decision-making tools, and handle large quantities of data for a more effective decision. For instance, in June 2023, according to Forbes, a US-based business magazine, approximately 432,000 UK organizations, or one in six, have adopted at least one AI technology. Further, at least one AI technology has been adopted by 68% of large businesses, 33% of medium-sized businesses, and 15% of small businesses. Therefore, the increasing adoption of artificial intelligence (AI) will drive machine learning (ML) in the pharmaceutical industry.

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Prominent Trend - Cutting-Edge AI-Integrated Platforms Unveiled By Major Players In The Pharmaceutical Industry

Major companies operating in machine learning (ML) in the pharmaceutical industry are focusing on developing user-friendly software platforms, such as drug discovery software, to improve their drug discovery capabilities. Drug discovery software is a broad term encompassing a variety of specialized tools and platforms used throughout the process of identifying and developing new pharmaceutical drugs. For instance, in December 2023, Merck & Co. Inc., a US-based pharmaceutical company, launched the AIDDISON drug discovery software, which is the first software-as-a-service platform that integrates drug discovery and synthesis using generative AI, machine learning, and computer-aided drug design. This platform enables laboratories to identify suitable drug candidates in a vast chemical space, virtually screen compounds from a universe of over 60 billion chemical targets, and evaluate synthesis routes for safer, more cost-effective, and higher-yield drug manufacturing.

Machine Learning (ML) in The Pharmaceutical Industry Market Players
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

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Largest And Fastest Growing Region In The Market
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 Table Of Content For The Market Report Include:
1. Executive Summary
2. Machine Learning (ML) in The Pharmaceutical Industry Market Report Structure
3. Machine Learning (ML) in The Pharmaceutical Industry Market Trends And Strategies
4. Machine Learning (ML) in The Pharmaceutical Industry Market - Macro Economic Scenario
5. Machine Learning (ML) in The Pharmaceutical Industry Market Size And Growth
…..
27. Machine Learning (ML) in The Pharmaceutical Industry Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix

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